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Piotr Gainski

Alumni

Publications

Scalable and Cost-Efficient de Novo Template-Based Molecular Generation
Piotr Gainski
Oussama Boussif
Andrei Rekesh
Dmytro Shevchuk
Ali Parviz
Mike Tyers
Robert A. Batey
Michał Koziarski
Template-based molecular generation offers a promising avenue for drug design by ensuring generated compounds are synthetically accessible t… (see more)hrough predefined reaction templates and building blocks. In this work, we tackle three core challenges in template-based GFlowNets: (1) minimizing synthesis cost, (2) scaling to large building block libraries, and (3) effectively utilizing small fragment sets. We propose **Recursive Cost Guidance**, a backward policy framework that employs auxiliary machine learning models to approximate synthesis cost and viability. This guidance steers generation toward low-cost synthesis pathways, significantly enhancing cost-efficiency, molecular diversity, and quality, especially when paired with an **Exploitation Penalty** that balances the trade-off between exploration and exploitation. To enhance performance in smaller building block libraries, we develop a **Dynamic Library** mechanism that reuses intermediate high-reward states to construct full synthesis trees. Our approach establishes state-of-the-art results in template-based molecular generation.
2025-12-02
Neural Information Processing Systems (Accept (poster))
doi.org
openreview.net
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 Hernández-García
2025-06-10
ICML.cc/2025/Workshop/GenBio (poster)
doi.org
openreview.net
Diverse and feasible retrosynthesis using GFlowNets
Piotr Gainski
Michał Koziarski
Krzysztof Maziarz
Marwin Segler
Jacek Tabor
Marek Śmieja
2025-04-13
Information Sciences (published)
doi.org
RGFN: Synthesizable Molecular Generation Using GFlowNets
Michał Koziarski
Andrei Rekesh
Dmytro Shevchuk
Almer 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.
2024-12-10
Conference on Neural Information Processing Systems (Accept (poster))
doi.org
openreview.net