Portrait of Piotr Gainski

Piotr Gainski

Alumni

Publications

Torsional-GFN: a conditional conformation generator for small molecules
Alexandra Volokhova
Lena Nehale Ezzine
Piotr Gai'nski
Luca Scimeca
Emmanuel Bengio
Prudencio Tossou
Yoshua Bengio
Alex Hernandez-Garcia
Generating stable molecular conformations is crucial in several drug discovery applications, such as estimating the binding affinity of a mo… (see more)lecule to a target. Recently, generative machine learning methods have emerged as a promising, more efficient method than molecular dynamics for sampling of conformations from the Boltzmann distribution. In this paper, we introduce Torsional-GFN, a conditional GFlowNet specifically designed to sample conformations of molecules proportionally to their Boltzmann distribution, using only a reward function as training signal. Conditioned on a molecular graph and its local structure (bond lengths and angles), Torsional-GFN samples rotations of its torsion angles. Our results demonstrate that Torsional-GFN is able to sample conformations approximately proportional to the Boltzmann distribution for multiple molecules with a single model, and allows for zero-shot generalization to unseen bond lengths and angles coming from the MD simulations for such molecules. Our work presents a promising avenue for scaling the proposed approach to larger molecular systems, achieving zero-shot generalization to unseen molecules, and including the generation of the local structure into the GFlowNet model.
2025-07-15
ArXiv (preprint)
arxiv.org
arxiv.org
Torsional-GFN: a conditional conformation generator for small molecules
Alexandra Volokhova
Lena Nehale Ezzine
Piotr Gainski
Luca Scimeca
Emmanuel Bengio
Prudencio Tossou
Yoshua Bengio
Alex Hernandez-Garcia
2025-06-11
ICML.cc/2025/Workshop/GenBio (poster)
doi.org
openreview.net
RGFN: Synthesizable Molecular Generation Using GFlowNets
Michał Koziarski
Andrei Rekesh
Dmytro Shevchuk
Almer M. van der Sloot
Piotr Gainski
Yoshua Bengio
Cheng-Hao Liu
Mike Tyers
Robert A. Batey
2024-09-25
NeurIPS.cc/2024/Conference (poster)
doi.org
openreview.net
RGFN: Synthesizable Molecular Generation Using GFlowNets
Michał Koziarski
Andrei Rekesh
Dmytro Shevchuk
Almer M. van der Sloot
Piotr Gainski
Yoshua Bengio
Cheng-Hao Liu
Mike Tyers
Robert A. Batey
Generative models hold great promise for small molecule discovery, significantly increasing the size of search space compared to traditional… (see more) in silico screening libraries. However, most existing machine learning methods for small molecule generation suffer from poor synthesizability of candidate compounds, making experimental validation difficult. In this paper we propose Reaction-GFlowNet (RGFN), an extension of the GFlowNet framework that operates directly in the space of chemical reactions, thereby allowing out-of-the-box synthesizability while maintaining comparable quality of generated candidates. We demonstrate that with the proposed set of reactions and building blocks, it is possible to obtain a search space of molecules orders of magnitude larger than existing screening libraries coupled with low cost of synthesis. We also show that the approach scales to very large fragment libraries, further increasing the number of potential molecules. We demonstrate the effectiveness of the proposed approach across a range of oracle models, including pretrained proxy models and GPU-accelerated docking.
2024-06-01
ArXiv (preprint)
doi.org
arxiv.org
RGFN: Synthesizable Molecular Generation Using GFlowNets
Michał Koziarski
Andrei Rekesh
Dmytro Shevchuk
Almer M. van der Sloot
Piotr Gainski
Yoshua Bengio
Cheng-Hao Liu
Mike Tyers
Robert A. Batey
Generative models hold great promise for small molecule discovery, significantly increasing the size of search space compared to traditional… (see more) in silico screening libraries. However, most existing machine learning methods for small molecule generation suffer from poor synthesizability of candidate compounds, making experimental validation difficult. In this paper we propose Reaction-GFlowNet (RGFN), an extension of the GFlowNet framework that operates directly in the space of chemical reactions, thereby allowing out-of-the-box synthesizability while maintaining comparable quality of generated candidates. We demonstrate that with the proposed set of reactions and building blocks, it is possible to obtain a search space of molecules orders of magnitude larger than existing screening libraries coupled with low cost of synthesis. We also show that the approach scales to very large fragment libraries, further increasing the number of potential molecules. We demonstrate the effectiveness of the proposed approach across a range of oracle models, including pretrained proxy models and GPU-accelerated docking.
2024-06-01
ArXiv (preprint)
doi.org
arxiv.org
RGFN: Synthesizable Molecular Generation Using GFlowNets
Michał Koziarski
Andrei Rekesh
Dmytro Shevchuk
Almer M. van der Sloot
Piotr Gainski
Yoshua Bengio
Cheng-Hao Liu
Mike Tyers
Robert A. Batey
Generative models hold great promise for small molecule discovery, significantly increasing the size of search space compared to traditional… (see more) in silico screening libraries. However, most existing machine learning methods for small molecule generation suffer from poor synthesizability of candidate compounds, making experimental validation difficult. In this paper we propose Reaction-GFlowNet (RGFN), an extension of the GFlowNet framework that operates directly in the space of chemical reactions, thereby allowing out-of-the-box synthesizability while maintaining comparable quality of generated candidates. We demonstrate that with the proposed set of reactions and building blocks, it is possible to obtain a search space of molecules orders of magnitude larger than existing screening libraries coupled with low cost of synthesis. We also show that the approach scales to very large fragment libraries, further increasing the number of potential molecules. We demonstrate the effectiveness of the proposed approach across a range of oracle models, including pretrained proxy models and GPU-accelerated docking.
2024-06-01
ArXiv (preprint)
doi.org
arxiv.org