no code implementations • 6 Dec 2024 • Krzysztof Maziarz, Guoqing Liu, Hubert Misztela, Aleksei Kornev, Piotr Gaiński, Holger Hoefling, Mike Fortunato, Rishi Gupta, Marwin Segler
Planning and conducting chemical syntheses remains a major bottleneck in the discovery of functional small molecules, and prevents fully leveraging generative AI for molecular inverse design.
2 code implementations • 26 Jun 2024 • Piotr Gaiński, Michał Koziarski, Krzysztof Maziarz, Marwin Segler, Jacek Tabor, Marek Śmieja
Single-step retrosynthesis aims to predict a set of reactions that lead to the creation of a target molecule, which is a crucial task in molecular discovery.
1 code implementation • 1 Jun 2024 • Michał Koziarski, Andrei Rekesh, Dmytro Shevchuk, Almer van der Sloot, Piotr Gaiński, 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 in silico screening libraries.
1 code implementation • 30 Oct 2023 • Krzysztof Maziarz, Austin Tripp, Guoqing Liu, Megan Stanley, Shufang Xie, Piotr Gaiński, Philipp Seidl, Marwin Segler
Automated Synthesis Planning has recently re-emerged as a research area at the intersection of chemistry and machine learning.
1 code implementation • 5 Jul 2023 • Piotr Gaiński, Michał Koziarski, Jacek Tabor, Marek Śmieja
Graph Neural Networks (GNNs) play a fundamental role in many deep learning problems, in particular in cheminformatics.
1 code implementation • 10 Feb 2023 • Piotr Gaiński, Klaudia Bałazy
We propose a novel gradient-based attack against transformer-based language models that searches for an adversarial example in a continuous space of token probabilities.
1 code implementation • 12 Oct 2021 • Łukasz Maziarka, Dawid Majchrowski, Tomasz Danel, Piotr Gaiński, Jacek Tabor, Igor Podolak, Paweł Morkisz, Stanisław Jastrzębski
Self-supervised learning holds promise to revolutionize molecule property prediction - a central task to drug discovery and many more industries - by enabling data efficient learning from scarce experimental data.