About us
We are a physics driven drug discovery company. Using advanced simulation software and AI-enhanced medicinal chemistry, we develop novel drug candidates and innovative mode of action against challenging targets. We design our drug candidates atom by atom to optimize their efficiency and their specificity with a favorable safety profile at unprecedented speed.
We build upon more than 30 years of R&D awarded by the most prestigious prizes such as 2 ERC grants and the ATOS Fourier prize. We spun-off from leading research institutions in France and in the USA : CNAM, CNRS, University of Texas at Austin, Sorbonne University and Washington University, in Saint Louis and we are based in Paris, at the very heart of “Le quartier Latin” and in Boston.
We are supported by three major deeptech investment funds Quantonation, Xange and Omnes Capital. In less than 18 months, we have built an impressive track record of awards such as the EIC accelerator thanks to our team and the technology.
We are embarking the most brilliant minds in theory, methods, software and drug design from both sides of the Atlantic to deliver on our mission and improve people’s lives.
The Founders
Pr Jay Ponder
Prof. Ponder’s research has a long-standing emphasis on the development and application of accurate molecular mechanics models for biomolecular structural analysis. Over the past several years his group has produced the
AMOEBA polarizable atomic multipole force field; one of the most accurate classical protein models currently available. Current applications of AMOEBA in the Ponder group include ab initio prediction of peptide and small molecule crystal structures, investigation of hydrophobic effects in liquid mixtures, and a series of problems related to drug design for particular systems of current biomedical importance. Of particular interest is the calculation of binding free energies for protein-ligand and host-guest systems. The force fields being developed for biomolecules are available through the Tinker and Tinker-HP software for CPU and GPU. In addition, by integrating computational and experimental approaches, he has studied protein-DNA assemblies in autoimmunity, biomaterials for tissue engineering, and novel inhibitors for protein kinases critical in cell signaling.
Pr Pengyu Ren
Professor Ren is E.C.H Bantel Professor for Professional Practice at UT Austin, and Fellow of American Institute for Medical and Biological Engineering. Dr. Ren is an author of over 100 publications, served as lead PI on several NIH and NSF projects. Ren lab’s research focuses on understanding the physical driving forces underlying biomolecular structures, dynamics, and interactions, and developing accurately physical models to engineer functional molecules and materials in silico. Along with Prof. Ponder, he was the first to develop an advanced electrostatic model for biomolecular simulations that incorporated atomic multipoles and many-body polarization effects and to demonstrate the accuracy of such models in prediction of protein-ligand recognition. Currently, he is extending AMOEBA to the next generation AMOEBA+ model with significantly improved functional forms and accuracy but at a reduced computational cost. The models and algorithms he developed are available through Tinker and Tinker-HP software for CPU and GPU computing. In addition, by integrating computational and experimental approaches, his lab has studied protein-DNA assemblies in autoimmunity, biomaterials for tissue engineering, and novel inhibitors targeting protein kinases in cell signaling.
Pr J.-P. Piquemal, CSO
Professor Piquemal is the prestigious winner of the 2018 Atos – Joseph Fourier Prize in High-Performance Computing as well as a winner of the 2018 European Research Council Synergy Grant for his work on Extreme-scale Mathematically-based Computational Chemistry (€10M). He is also a Member of the Institut Universitaire de France, and is currently the director for the Laboratoire de Chimie Théorique at Sorbonne Université . He is a recipient of the Wiley – International Journal of Quantum Chemistry Young Investigator award. His research is devoted to theoretical chemistry, applied mathematics, software development innovations in multiscale quantum chemistry for large systems, new generation polarizable force fields, and quantum chemical topology. He is the lead developer of the Tinker-HP software, a massively parallel version of Tinker allowing for fast molecular simulations on new-generation supercomputers and GPUs cards.
Dr Louis Largardère
Dr. Lagardere has been trained in Applied Mathematics at the Ecole Nationale des Ponts et Chaussees and holds a Ph.D. in theoretical chemistry from Sorbonne University. He is a co-recipient of the Atos Joseph Fourier 2018. Since 2016, he holds a permanent Research Engineer position in Paris and works as the lead programmer for the Tinker-HP project. His expertise lies in high-performance computing (C3I GENCI fellow), theoretical chemistry and applied mathematics. He is the author of more than 30 scientific papers and leads the engineering team in the EMC2 European project.
Pr Matthieu Montes
Prof Matthieu Montes, leads the molecular modeling and drug design team of the GBCM lab at Conservatoire National des Arts et Métiers, Paris, France. His research interests include molecular modeling, drug discovery and design, interactive visualization and simulation methods, and computational geometry. He is a recipient of the prestigious SCT award for Young Investigators in Medicinal Chemistry. Since 2014, he is a fellow of the European Research Council for his work on the ViDOCK project focused on applying mathematics and computer graphics methods in computational biology and chemistry (€1.5M). He is a co-author of 50 publications and co-inventor of 7 patents for therapeutic products in cancer and inflammation
The Team
We have assembled an international team with significant scientific and industrial expertise. Our complementary expertise covers all the disciplines involved in drug discovery and ranges from quantum chemistry and physics, HPC, computer-aided drug design, medicinal chemistry, artificial intelligence and structural biology.
Lina
PhD in molecular modeling, I am passionate about disruptive innovations happening within computational biology domain, and convinced about the efficiency of recent approaches involving artificial intelligence aiming to accelerate the search for valuable drug candidates.Holding a double master degree in biotechnology and bioinformatics respectively from the Université de Technologie de Compiègne and the Université Paris Diderot, I joined the CEA Genoscope, where I have worked on big data techniques applied on genomic bioinformatics subjects. My eagerness to explore structural bioinformatics, led me to specialize in molecular modeling through the prestigious doctoral competition of the Université Paris Descartes.As part of my thesis, I contributed to the development of an integrative molecular modeling solution to predict the 3D structures of biomolecules, in particular RNAs.
Understanding the relationship between RNAs activity and their structures, has become increasingly targeted in the drug discovery. In addition, to mRNAs coding for protein synthesis, there are many families of non-coding RNAs, each of which folds into specific spatial conformations. Due to the large size and high flexibility of some RNA molecules (particularly non-coding RNAs), their structures remain hard to determine through experimental techniques. Integrative molecular modeling consists in implementing experimental data of different resolution levels during a molecular simulation to guide an RNA model trajectory towards its target.After my PhD, I have joined Qubit Pharmaceuticals, as a research scientist, within the molecular modeling team in charge of conducting drug discovery programs.
Elettra
My university studies at La Sapienza of Rome have been devoted to foreign languages and literatures. Through this humanistic approach, I discovered human nature and the beauty of a world that is varied and changing according to the perspective with which one looks at it.
With a Master in my pocket, through the European Leonardo program, I landed in France, at the International relations office of the ENS Lyon, where I started my professional career as the administrative coordinator for the AToSim Erasmus Mundus project. This was a Master program in Physics for non-European students held in 3 universities: the ENS Lyon, the Universiteit van Amsterdam and the University of La Sapienza of Rome.
It has been a cardinal experience. Beyond using all the languages I knew, I learnt from my colleagues (all extraordinary and well qualified people) the secrets for a well-done administrative management and I interiorized the satisfaction of doing my best in my job.
Two years later, I moved to Paris, to the Foundation Pierre-Gilles de Gennes for Research. There was where I improved my skills, took on more responsibility and learnt to work independently. I kept working with PhD students and Postdoctoral researchers coming from Europe and from extra-European countries, dealing with VISA matters, university conventions, fellowships award, call for projects… But I was also involved in the management of the budgets of the subsidized programs, as well as in the accounting and administrative management of the foundation.
Then it was time to move on, to be the Office Manager for the SATT Ile-de-France Innov. During the following 5 years, I improved my skills in administrative management and accounting. And I deeply plunged into the human resources management. It was a very formative experience, a period of all-round personal and professional development.
Having then fully acquired my versatility and developed the ability to manage several tasks autonomously and responsibly, I was ready to enter into the beauty of architecture. And I had the opportunity to do so by being in the privileged position as a family assistant for Senator Arch. Renzo Piano.
For the next three years, I dedicated myself to the management of the architect’s personal assets, and to the related tax issues in France, and abroad; I managed the personal staff, and took care to maintain effective communication between the Renzo Piano Foundation, the RPBW offices in Paris and in Genoa and the Piano family. I was at the interface between the personal and the professional side while I took care of everything that was related to Senator Arch. Piano’s family everyday life.
It has been busy years, with professional achievements and improvements, made in a creative context and supported by extraordinary colleagues.
Yet, it was time to go on and move again.
So, what brought me to Qubit Pharmaceuticals?
- I know Robert Marino since my arrival in Paris. I trust his ideas and his ability to recognize skills and manage them to achieve the goal.
- I see a scientific potential in the company and believe in a future success of it
- I want to get involved in building a company that can make a difference
- I want to give my skills another boost
Let the new adventure begin!
Ekaterina
Axel
Always been attracted by both Physics and Mathematics, I naturally studied them at Sorbonne University (BSc in Physics) and Aix-Marseille University where I passed my MSc in theorical Physics. It gave me precious tools for a better understanding and ability of Mathematics and Physics (and noteworthy in Quantum Physics).
During those years, I also co-produced a book on mathematical methods for Physicists at the (under)-graduate level addressed for both theoretical and experimental purposes and worked on various theoretical subjects. I focused on three different open research studies.
The first one was the resolution of spin models, particularly the Ising model, starting from analytical approaches to qualitative resolutions up to 3-dimensions. Concerning the 3-dimension case, I participate with Dr. Vladimir S. Dotsenko (Sorbonne University) in some attempts of an analytical resolution of the Ising model using conformal theory and a special case of free fermion string theory approach, following his common idea with Dr. Alexander Polyakov.
The second study on which I was involved during my MSc graduation, treated a relatively new and uncommon approach of mathematical description of gauge theories focusing on the purpose to obtain a common structure between both gauge theories of the standard model and of General Relativity. This idea lies on the use of Lie Algebroids in the frame on non-commutative geometry to find a unique representation of both gauge theories, where the one from the standard model is the usual Yang-Mills theory while the one for General Relativity is expressed in the frame of the Einstein-Cartan theory.
Finally, my last and most important study was performed during my internship at the Observatoire de Paris in collaboration with the Perimeter Institute (Toronto, Canada). This study was in the framework of massive gravity theories, being theoretical extension to General Relativity (GR), by allowing the graviton (hypothetical gravitational wave corresponding particle from the quantum wave-particle duality) to bear a small but significant enough mass. More precisely, I worked on analytic computations of gravitational waves characteristic perturbations due to the added mass. In addition, I also related those computations to a general formalism that links theoretical gravitational wave results to experimental values obtained from the LIGO/VIRGO detectors. In theory, it shall allow at the end to test the validity of the massive theories, constraint precisely the possible mass of the graviton, and reinforce the validity of GR in non-quantum theory of gravity.
Then, as I was looking for a new challenging experience, I joined the Quantum team of Qubit Pharmaceuticals to participate to a more concrete ambition. Thus, I hope that, through my theoretical backgrounds in both Physics and Mathematics, I will bring expertise, efficiency and help to the technology used by the entire team in their purpose of drug discovery breakthroughs.
Pierre
Computational chemist and cheminformatician, I’ve always been passionated by life sciences and informatics. Today, I combine my knowledge in biochemistry, chemistry and data science to discover new drugs.
After a master degree in biochemistry and biophysics, during which I was introduced to computational chemistry, I obtained a PhD in organic chemistry from the University Paris XI/ICSN-CNRS, followed by two years post-doc at FSU and Institut Curie, mainly focused on Oncology drug discovery using 3D-QSAR and structure-based design methods. I then joined the pharmaceutical industry, Pfizer then Sanifi, as a computation chemist, cheminformatician and drug designer, where I used machine learning methods such as SVM, RF and BRANN to build models and predict compound properties. As team lead, at Servier then AstraZeneca, I built successful modeling and cheminformatics teams and led the cheminformatics-AI platform. I initiated and led multiple collaborations, PhD thesis and post-docs with universities and small companies across Europe, to develop new methods for drug discovery.
During my career, I’ve been involved in multiple drug discovery projects, working on targets such as enzymes, GPCRs, or nuclear receptors, ion channels in oncology, inflammation and cardiovascular diseases.
I recently joined Qubit-Pharmaceuticals as Drug Discovery Program Lead with the goal of rapidly identify pre-clinical drug candidates, using physics-based method to predict drug properties, combined with state-of-the art algorithm to design and optimize compounds on multiple properties.
Dina
It took me a bachelor’s degree in biochemistry and life science and a master degree in functional genomics and proteomics to realize that I’m fascinated by molecular modelisation and drug design which I made my expertise in during my PhD.
During my PhD, I’ve studied DNA Guanine Quadruplexes in oncogenes and retroviruses and SARS-CoV-2 Main Protease using Molecular Dynamic (MD) simulations coupled to Adaptive Sampling Algorithm employing AMOEBA (Atomic Multipole Optimized Energetics for Biomolecular Simulation) polarizable force field implemented in Tinker-HP. In fact, studying the conformational changes of apo structures of nucleic acids and proteins is a key step to understand our targets before designing any potential durg. Besides MD, I’ve contributed to the parametrization of another potential polarisable force field called SIBFA (Sum of Interactions Between Fragments Ab initio computed) based on all amino acid fragments and DNA nucleobases.
After my joint PhD in Computational Chemistry and Molecular Modeling from Sorbonne University (Paris 6) and in Life Science from Saint-Joseph University of Beirut, I joined Qubit Pharmaceuticals to contribute to the well being of the human race using the most advanced technologies, and the fastest, to develop novel therapeutic drugs toward the most dangerous and untreated diseases. I believe that the future is already the present as we’ve already started to use an AI/ML workflow capable of treating millions of compounds and to select only 1-10 candidates in only a few months.
As I do have a particular interest in communication and transmitting scientific knowledge outside of the close community, I intend to close the communication gap between the scientific and the non-scientific communities to develop a trustful relationship between us and the world.
Krystel
Dr. Krystel El Hage joined Qubit Pharmaceuticals in October 2022 as a Project Leader in Drug Discovery. She holds a PhD in Molecular Modelling from Université Paris Descartes. Her expertise covers a wide range of computational and experimental techniques as well as a large variety of targeted systems. She is the author of more than 35 scientific papers and has 10+ years of experience in molecular modeling and drug design (University of Basel, University of Lausanne/Ludwig cancer research center, Université Paris-Saclay/INSERM). In 2020, she was awarded a Marie Sklodowska-Curie Individual Fellowship to lead an EU-funded project focusing on identifying small molecules inhibiting translation in cancer cells by targeting the RNA-binding interface of RNA-binding proteins.
Léa
My research interests are largely directed towards predicting biological processes such as protein-ligand binding and unraveling the chemical and physical factors governing biomolecular recognition.
As a joint graduate student at Sorbonne Université and Saint-Joseph University of Beirut, I have combined experimental and computational methods to study the affinities of inhibitors of therapeutic interest. I have also contributed to the parametrization of the SIBFA (Sum of Interactions Between Fragments Ab initio computed) polarizable force field and my work mainly focused on optimizing the force field parameters to enable accurate molecular modeling of metalloprotein’s active sites. During my PhD, I worked in the team led by Prof. Jean-Philip Piquemal, with scientists from different disciplines, to apply a massively parallel version of a molecular dynamics package (TINKER-HP) to study the structural and conformational changes of protein complexes.
After defending my PhD, I joined Prof. Mobley Lab at University of California Irvine, where I worked on drug discovery projects using docking, molecular dynamics approaches, enhanced sampling techniques, and binding free energy calculations. One of these projects was our participation to the D3R Grand Challenge, where we developed and tested a combined docking-molecular dynamics workflow to predict the binding modes and binding affinities of a series of macrocyclic ligands towards ß-amyloid precursor protein cleaving enzyme 1 (BACE-1) involved in Alzheimer’s disease. Additionally, I explored the structural and functional changes of several proteins upon binding, performed absolute binding free energy calculations on host-guest systems and protein-ligand complexes, and worked on protein redesign.
I joined Qubit Pharmaceuticals because I am interested in using polarizable force fields to work on a wide range of therapeutic biological targets, including challenging metalloenzymes. I believe that our in-house developed platform, ATLAS, will make a significant shift in the way drugs are developed.
César
My name is César and I am a junior research scientist for Qubit Pharmaceuticals. I studied chemistry at the Ecole Normale Supérieure Paris-Saclay where I majored in computational chemistry. I also graduated with an interinstitutional Year of Research in Quantum Technologies (ARTeQ) degree where I was trained in quantum computing and algorithms, quantum hardware and advanced quantum mechanics under Pr. Jean-François Roch and Pr. Alain Aspect. These interdisciplinary skills led me to the field of quantum algorithms for chemistry. I took my first steps in this subject during an internship at the University of Cambridge under Pr. Alex Thom. Here, I implemented a quantum/classical hybrid algorithm called Variational Quantum Eigensolver for quantum chemical calculations as well as developing novel optimizing strategies.
I deepened my learning during a second research internship with Laboratoire de Chimie Théorique (LCT) at Sorbonne Université under the direction of Pr. Jean-Philip Piquemal. Our project was to bring improvements to state-of-the-art adaptative quantum algorithms for quantum chemical simulations. I am now pursuing this project as a PhD student with Sorbonne Université and Qubit Pharmaceuticals since October 2022.
I am extremely excited to take part in the challenge of outperforming classical computing on a range of chemistry simulations with quantum computers. I strongly believe that the Qubit Pharmaceuticals team can make a significant contribution to the field in the coming years.
Jérome
I spent almost 20 years in the field of computer science applied mathematics AI, ML and entrepreneurship. I first had my PhD degree in 2003 in Robotics where I developed a totally new approach to characterizing and controlling legged robots’ stability at any gate by introducing an operator that generalized all former approaches. I then worked for Thales in HPC where I participated in the design and development of a real-time development framework involving massive parallelism and distributed computations. I then combined both my mathematical and computer science skills in the field of finance developing and implementing stochastic algorithms for CIB derived financial products. I spent my past 10 years in founding and leading, at a C-level, companies in advanced computer science using AI, HPC and ML addressing reverse engineering and automatically generated code at scale. I led teams of more than 20 people, and conducted more than 60 projects in the field of reverse engineering of programming languages. Starting from scratch I ended up with several million revenues in less than 5 years. I finally joined Qubit-Pharmaceuticals in 2021 as the Chief Product Officer (CPO) to lead the design and development of the computer aided drug discovery platform: Atlas. As a C-level, I am in charge of several operational aspects of the company: involved in fundraising campaigns, recruitment process, project management and lead, knowledge management and sharing. Atlas being central at Qubit-Pharmaceuticals, all AI, ML, HPC, quantum computing and R&D subjects remain my central topics of interest and activity.
Louis
My name is Louis Geisler. I am a Junior Data Scientist at Qubit Pharmaceuticals and I work with Boris to build new machine learning models.Our goal is to build an efficient <a href=”https://en.wikipedia.org/wiki/Quantitative_structure%E2%80%93activity_relationship“>QSPR (quantitative structure–property relationships)</a> to speed up the drug discovery process.Artificial intelligence-assisted drug discovery is a relatively new, very promising and also very challenging field.
Nicolai
I studied mathematics at Sorbonne University in Paris. After my fourth year, I also prepared and obtained the Agrégation (French competitive exam for teachers), which allowed me to consolidate my knowledge in various areas of mathematics (algebra, geometry, calculus, probabilty theory…), helped me to acquire a transverse insight in those subjects and to build mental bridges between the different sub-domains of mathematics. I then specialized Probability&Statistics for the last year of my Master’s degree, where I studied Markov processes, stochastic calculus, statistical learning, concentration inequalities, Bayesian statistics and Markov chain mixing times.
In May 2021 I started a 5 month internship at Qubit Pharmaceuticals in collaboration with LJLL (Laboratoire Jacques-Louis Lions) and LCT (Laboratoire de chimie théorique) at Sorbonne University. During that internship I worked on Monte-Carlo methods for sampling probability distributions in the framework of quantum computing, trying to answer the question whether a quantum computer can achieve an algorithmic speedup over classical methods, and if such a method could be implemented on an actual near-term quantum device. Monte-Carlo methods are a key component in computing free energy differences, which are essential in molecular dynamics and drug discovery. Efficient implementation of such algorithms on quantum computers could not only accelerate drug discovery, but also allow us to spend a lot less energy in calculations.
In october 2021 I started a PhD on the same subject. My research is at the interface of probability theory (stochastic algorithms, Monte-Carlo methods, Markov processes), quantum computing and chemistry (molecular dynamics, free energy calculations).
My goal is to bring my expertise in probability theory and stochastic processes to help to create the link between theoretical quantum computing, sampling problems and Monte-Carlo methods, and applied computational chemistry.
Florent
I started by studying chemistry for 3 years (2006-2009) at Université de Picardie Jules Verne (Amiens, FR): at this time, I was already much more interested in understanding the theory and performing first numerical simulations than performing lab experiments. This logically led me to Université de Strasbourg (FR) for a Master in Cheminformatics where I discovered Computational Chemistry: Molecular Dynamics, QSAR/QSPR, Chemical Databases… a fascinating world! I then went to the University of Basel (CH), Physical Chemistry Department, for a 6-month Master internship, working on Monte-Carlo simulations applied to Biomolecules… and stayed there for my PhD (2011-2016), where my main research topic was the development and usage of accurate rare-event sampling methods focusing biologically relevant chemical systems. After obtaining my PhD I went to Ecole des Ponts – CERMICS (FR) for a two-year postdoc (2016-2018) with mathematicians (that was an interesting challenge), where I coded & published the first publicly available implementation of the Generalized Parallel Replica algorithm (it can for instance be used for accurately estimating protein-ligand unbinding times). And finally, I worked for two years (2018-2020) at Inria Paris as a Research Engineer on the implementation and optimization of a software used for studying flowing in fractured rocks.
Always willing to learn new things, and not afraid by new challenges, I joined the Qubit Pharmaceuticals team in Nov 2020 with my 10+ years background in theoretical chemistry & informatics: I am in charge of the setup and development of the High Performance Computing (HPC) framework behind our unique simulation platform Atlas.
Francis
I joined Qubit Pharmaceuticals after completing my PhD at the University of Texas at Austin, advised by Prof. Pengyu Ren. My expertise includes molecular dynamics simulations, force field development, quantum mechanics, protein-ligand binding and nano-materials.
I studied chemical engineering in college with the dream of designing molecules and materials that could solve every problem. I was not impressed by the chemistry courses until I was introduced to computational chemistry. I immediately realized that computational chemistry was the future, at a time when it was considered a toy by most chemists. However, I did not know that it would be take tremendous effort to make computational chemistry actually useful.
I conducted undergraduate research about density functional theory and nano-clusters. Then I was convinced by my Master’s advisor that to make useful predictions for biological and industrial problems, we should go beyond density function theory and embrace the principles of statistical mechanics through methods like molecular dynamics and Monte Carlo simulations. So I learned statistical mechanics by myself, which turned out to be one of my favorite subjects. During my Master’s study, I was involved in the development of a general force field for small molecules and polymers, theoretical analysis of enhanced sampling methods, and computational studies of zeolite nano-materials. In these studies, I could predict or explain experimental observations, but most of the results were still semi-quantitative due to the limitation in model accuracy. This time period also coincided with the emergence of general-purpose GPUs and the award of the Nobel Prize in Chemistry to pioneers of molecular dynamics simulations, which helped boost the popularity of the field. As computational power continued to grow, large-scale simulations with more sophisticated models started to become accessible. So I joined Prof. Ren’s lab to study polarizable force fields. I was fortunate to solve some long-standing problems regarding protein-ion recognition and ion channels, since it was only possible by applying accurate models such as polarizable force field. Besides, I also enjoyed learning about new methods and research areas. I have utilized simulations to solve many other problems regarding polymer-drug conjugates, immunotherapy, gene therapy and therapeutic nano-materials.
I am glad to be part of Qubit Pharmaceuticals to continue exploring the forefront of physics-based simulations and to achieve the next breakthrough in drug discovery.
Jean
I have been a computer science engineer since 2013. Once I graduated I started writing software to operate radio transmitters in airports networks called critic systems, making sure that pilots and control tower operators could communicate properly and safely. Since then I had a growing interest in signal processing so I moved to join a compagny delivering various audio effects solutions to a large public. I worked on both the application and the driver sides and also optimized computations in embedded chips.
Here at Qubit I hope to help by bringing advanced 3D modelling technologies to explore molecules with the highest precision computers can offer. I enjoy applying theorical concepts to the reality and write custom solutions to problems with multiple constraints.
Daniele
I studied Chemistry in Italy, where I got first my B.Sc. degree at University of Pavia, moving then to the University of Pisa where I earned a M.Sc. degree in Physical Chemistry. After that I started a PhD in “Scienze Chimiche e dei Materiali” at the same University, working on the development and application of polarizable QM/MM molecular dynamics, in order to study environment effects on spectroscopic properties of chromophores embedded in biological environments. After implementing the methodology in two suites of programs coupled for this purpose (a developing version of Gaussian09 and Tinker) I have studied the coupled environment and electron-nuclear dynamics of a fluorescent probe used to detect DNA. With the same tools I have contributed to the computational investigation of the solvatochromism of long-chain carotenoids embedded in the crustacyanin protein, responsible of the colors of the exoskeleton of lobsters (or of they nice red color when they are cooked).
After completing my PhD I moved to Paris, to work as postdoctoral fellow in the research group of Prof. Piquemal, to further develop the QM/MM dynamics I worked in a multidisciplinary environment, working in close contact with mathematician to develop advanced algorithm and novel molecular dynamics approaches. I have included the treatment of QM/MM cross-bondings, so to deal with systems where the chemically active region of interest in a large macromolecule form a covalent bond with the rest of the environment. In the near future more has to come on the work done on newer version of advanced force fields to improve the description of molecular interactions in QM/MM and on a mixed atomistic/implicit treatment of the environment in classical dynamics!
Since those two experiences, my interests have been directed toward all the implications of the following, short fictitious dialogue between Scientist A and Scientist B:
Scientist A: “This is too much to be precise enough!”
Scientist B: “Maybe…”
In fact I like to apply and improve molecular modeling techniques aiming at the study of properties and behaviors of biologically relevant molecules. A way to achieve it is merging different, but very well connected, areas of Computational Chemistry: Molecular Dynamics based on Polarizable Force Fields, Quantum Chemistry to achieve an accurate description of physico-chemical events and the embedding theory, to combine the former two, so to deal with large molecular aggregates or biomolecular systems, keeping a good description of the phenomena of chemical interest.
A large part of my work has been dedicated to implement embedding models or molecular dynamics algorithms in computer codes, coding most of the time in some version of Fortran (as FORTRAN 77). I still enjoy very much to write new code and later see the results applied to a problem of interest for its scientific value.
I also enjoy to try new recipes, mostly of French and Italian cuisine, go to the movie theater or to progressive (-ish) concerts.
Thomas
Fascinated by Mathematics and computer sciences, I started my studies at ENSICAEN with the Computer science main courses specialized in Image processing. I discovered many programming languages going from LISP to C#.
Graduated by the end of 2012, I started to work in finance with Margo for Sgcib, Engie and then in payments with Sopra Banking Software. During these years, I worked on large projects : quant software factory (C#), market data retrieval for commodity traders (C#), financial flow management for banks (Java), and finished with a credit management app for car dealerships (java/React).
Now in Qubit Pharmaceuticals as a fullstack developer, I’m pleased to bring my expertise to the drug discovery field, which was few years ago pure sci-fi.
Antoine
I have always been passionate and inspired by the great potential of computing in terms of thinking and designing. That’s why I made Computer Science studies, and then worked in IT, including self-learning in associated disciplines like User Interface problematic, gaming, data architecture, data viz, communication, and even graphic design.
My objective is to make the computer a great assistant for thinkers, researchers, and designers. I like this tool to be unobtrusive, efficient, and pleasant to use. I found a good way to satisfy my expectations by joining this amazing company and its stimulating people.
Robert
Robert Marino, PhD, is the CEO of Qubit Pharmaceuticals. He has 12 years of experience in startup development & investment (BU Manager at TTO Office, €12m invested in 40 projects); business development (head of industrial partnerships of the Fondation Pierre Gilles de Gennes). Robert also cofounded Deeptech Founders, a leading deeptech acceleration program in France (200 deeptech projects accelerated in 36 months, €150m raised by the first startups) and le Lab Quantique a non for profit to support the emergence of a global quantum ecosystem. Robert holds a Master of Science in chemical engineering, a Master of Science in applied & fundamental physics and did a PhD on the development of Quantum Memories using rare earth doped single crystals .
Clara
Driven by the desire to explore where new machine learning applications meet innovations in the health sector.
I joined Qubit Pharmaceuticals for an internship during my engineering degree in Biotechnology at Sup’Biotech Paris, where I specialized in the program Biotech&Numeric in partnership with the engineering school ESME Sudria. My specialization aims to train creative and multidisciplinary engineers to tackle today’s new challenges in the health sector by being able to work at the interface between Informatics and Biotechnology.
Now at Qubit Pharmaceuticals, I have the exciting opportunity to contribute to drug design by applying the most advanced computational chemistry and machine learning techniques.
Yassine
My first university degree, a master degree of science, was in genetics and immunology from the university of Rouen, in France. In this context, I performed several internships on applying oral tolerization protocols in the context of gene therapy which led to my first participation in a scientific publication.
Being deeply passionate about computer science since my childhood, I decided to get a look at the bio-informatic field because it perfectly combines my two main area of interest: biology and computer science. For this purpose, I completed two other master’s degree, a first one in computer science, delivered by Aix-Marseille University, during which I had a deeper and broad teaching with strong basis in algorithmic along with the best practises in the computer science industry. This degree gave me the right bases to complete a second master’s degree in in silico drug discovery, delivered by Paris Diderot University during which I had a very rich internship at Sanofi and a first insight of the pharmaceutical industry with the most used molecular modelling techniques.
I then worked at Inria during two years before co-founding OneAngstrom, a start-up commercializing the SAMSON software platform. During that time, I integrated to the SAMSON platform many well-known and widely used molecular modelling tools like GROMACS, RDKit or Hex as well as academic tools which were only used by few persons in order to make them more accessible to more persons around the world. Thanks to that experience in integrating different technological blocks, I joined Qubit Pharmaceuticals, in order to take on the challenge of creating the world first drug discovery platform running on quantum computers and based on several cutting-edge and independent blocks. At Qubit Pharmaceuticals, I am working on the Atlas platform by integrating scientifically validated technological bricks and making them communicate in the most efficient way to get the best out of them.
Evgeny
While acquiring Bachelor (2011-2015) and Master (2015-2017) degrees in Applied Mathematics and Computer Science, I developed a strong interest in the area of numerical simulations and high-performance computing. This drove my motivation to complete a PhD thesis (2017-2020) in Chemical Physics with the major focus on large-scale quantum dynamics of excited molecular systems.
Over the years, I accumulated valuable hands-on experience in applying my analytical and programming skills to tackle challenging problems in computational sciences. The codes that I developed have been used to (i) model industrial devices for spent nuclear fuel reprocessing; (ii) simulate the topological effects in superconductors; (iii) perform quantum molecular dynamics at large scale and (iv) enhance the performance and interoperability of quantum chemistry programs.
In October 2022, I joined Qubit Pharmaceuticals as a senior HPC engineer. It is exciting to be part of a strong international team, which is revolutionizing the drug discovery using the cutting-edge software and hardware solutions deployed via Atlas. I am passionate about many software development concepts including high-performance computing (C, Fortran, Rust), containers (Docker, Singularity), machine learning (Python) and databases (SQL). Among other things, I enjoy snowboarding, reading and hiking.
Manon
I am specialized in structural bioinformatics and chemoinformatics an more precisely in the study of small molecule/ protein and protein/protein interactions.I discovered the world of bioinformatics during my studies at SupBiotech Paris where I obtained a master degree in Biotechnology. I specialized in In Silico Drug Design through one year of internships at the University of Zürich under the supervision of Pr Amedeo Calfisch and at the CNRS under the supervision of Dr. Marc Baaden, completed with a master degree (Master In Silico Drug Design) at the Université Paris Diderot. During these studies I got acquainted with MD simulations and various virtual screening approaches, including fragment- and ligand-based molecular docking as well as pharmacophore modelling.I joined the Conservatoire National des Arts et Métiers (Cnam) in 2016 as a PhD candidate within the group of Pr. Matthieu Montes where I studied the impact of considering experimentally validated negative data on the design and the evaluation of in silico models. Most of my work was applied to the Nuclear Receptor family and enabled proposing highly selective models for agonist compounds of the Androgen Receptor.After obtaining my PhD, I joined the group of Pr Alexandre Bonvin at the University of Utrecht for two years. I got acquainted with deep learning algorithms and co-developed DeepRank-GNN, a graph neural network to learn and predict information from protein/protein interfaces. I also co-developed shape-restrained modelling protocols for protein-small molecule complexes with HADDOCK.I joined the Drug Discovery Team of Qubit Pharmaceutical in March 2022.
Boris
My name is Boris Sattarov. I’m a cheminformatician at Qubit Pharmaceuticals specializing in applying Machine Learning (ML) to chemical and biological data in drug discovery. Before joining Qubit, I led cheminformatics and ML efforts in 2 companies in North America and worked in 3 academic labs in Europe. My professional background allowed me to gain expertise and publish in various domains of applying ML to drug discovery, from reaction-based molecular generators to ligand- and structure-based drug discovery. Through trial and error, I’ve learned that ML in drug discovery is exceptionally far from simple fit/predict due to the biases in data collection and the general lack of clean and standardized datasets. Therefore, I believe in clean, interpretable, and thoughtfully designed modeling pipelines that allow us to make better decisions and enhance our natural chemical intuition.
I’m incredibly excited to take on challenging problems in ML-based drug discovery together with our outstanding team at Qubit Pharmaceuticals. One of the main competitive advantages of Qubit is highly optimized software for accurate absolute binding free energy calculation, which allows for unbiased data collection and predictive modeling with a vast applicability domain in the chemical space.
Siri
Driven by the structural understanding of protein function and leveraging this knowledge in drug development, I have geared my expertise towards protein-protein and protein-ligand interactions as well as conformational rearrangements. As a computational biochemist I have over 10 years of experience in this field and 16 publications, applying protein modelling, molecular dynamics simulations and docking to proteins and their ligand interactions.
The G-protein coupled receptor (GPCR) signal transduction pathway, a common pharmaceutical target, was the focus of my PhD in the laboratory of Prof. Ursula Röthlisberger at École Polytechnique Fédérale de Lausanne (EPFL). I investigated GPCR activation as well as the impact of G proteins on adenlyl cyclase regulation using quantum and classical molecular dynamics simulations. Next, as a postdoctoral researcher at Stanford University (Prof. Ron Dror), I applied microsecond molecular dynamics simulations to study GPCR kinase regulation. Furthermore, in the group of Prof. Alexandre Bonvin at Utrecht University, I used protein-protein and protein-peptide docking as well as molecular modelling to setup docking protocols and predict protein complexes.
At Qubit Pharmaceuticals, I now have the exciting opportunity to contribute to the design of drugs using the latest technical developments in the field of computational chemistry and artificial intelligence. It feels great to be part of this drug development adventure!
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also
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