Piotr Gaiński — accepted AI-conference papers

2025

Scalable and Cost-Efficient de Novo Template-Based Molecular Generation

Piotr Gaiński, Oussama Boussif, Andrei Rekesh, Dmytro Shevchuk, Ali Parviz, Mike Tyers +2

NeurIPS 2025poster

Template-based molecular generation offers a promising avenue for drug design by ensuring generated compounds are synthetically accessible through predefined reaction templates and building blocks. In this work, we tackle three core challenges in template-based GFlowNets: (1) minimizing synthesis co…

Cited by 0 Source ↗code ↗Scholar ↗

2024

RGFN: Synthesizable Molecular Generation Using GFlowNets

Michał Koziarski, Andrei Rekesh, Dmytro Shevchuk, Almer M. van der Sloot, Piotr Gaiński, Yoshua Bengio +3

NeurIPS 2024poster

Generative models hold great promise for small molecule discovery, significantly increasing the size of search space compared to traditional in silico screening libraries. However, most existing machine learning methods for small molecule generation suffer from poor synthesizability of candidate com…

Cited by 13 Source ↗PDF ↗code ↗Scholar ↗