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.
Pr Jay Ponder
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
Pr J.-P. Piquemal, CSO
Dr Louis Largardère
Pr Matthieu Montes
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.
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!
I joined Qubit Pharmaceuticals as a Project Owner in August 2022. I hold a PhD in Theoretical Physics from CEA Saclay (2014) and I have got 8 years experience in startup development and product management in the AI field. I’m Co-Founder and former Chief Product Officer of QuantsUnited (AI applied to financial markets), successfully acquired by Synthetic Neural Labs Inc in 2021. I met Robert Marino and Jerome Foret during the acceleration phase at Deep Tech Founders in 2019. Before QuantsUnited I was COO and Data Scientist at DreamQuark (AI for FinTech). I am Co-Founder and former CEO of DreamUp Vision (AI in Healthcare spin-off of DreamQuark), dedicated to diabetic retinopathy detection. Got several start-up awards, including 2016 Challenges Magazine, US SXSW 2016, TheFamily 2015, Monaco 2016, invited speaker of major A.I. and Data science conferences, scientific awards and several awards for best students of Moscow State University. Published scientific papers on Theoretical Particle Physics as well as on Machine Learning.
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.
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.
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.
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.
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.
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.
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.
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.