Zobrazeno 1 - 6
of 6
pro vyhledávání: '"Gaiński, Piotr"'
Autor:
Gaiński, Piotr, Koziarski, Michał, Maziarz, Krzysztof, Segler, Marwin, Tabor, Jacek, Śmieja, Marek
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. Although a target molecule can often be synthesized with multiple different reactions, it is
Externí odkaz:
http://arxiv.org/abs/2406.18739
Autor:
Koziarski, Michał, Rekesh, Andrei, Shevchuk, Dmytro, van der Sloot, Almer, Gaiński, Piotr, Bengio, Yoshua, Liu, Cheng-Hao, Tyers, Mike, Batey, Robert A.
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
Externí odkaz:
http://arxiv.org/abs/2406.08506
Autor:
Maziarz, Krzysztof, Tripp, Austin, Liu, Guoqing, Stanley, Megan, Xie, Shufang, Gaiński, Piotr, Seidl, Philipp, Segler, Marwin
The planning of how to synthesize molecules, also known as retrosynthesis, has been a growing focus of the machine learning and chemistry communities in recent years. Despite the appearance of steady progress, we argue that imperfect benchmarks and i
Externí odkaz:
http://arxiv.org/abs/2310.19796
Graph Neural Networks (GNNs) play a fundamental role in many deep learning problems, in particular in cheminformatics. However, typical GNNs cannot capture the concept of chirality, which means they do not distinguish between the 3D graph of a chemic
Externí odkaz:
http://arxiv.org/abs/2307.02198
Autor:
Gaiński, Piotr, Bałazy, Klaudia
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. Our algorithm mitigates the gap between adversarial loss for continuous and disc
Externí odkaz:
http://arxiv.org/abs/2302.05120
Autor:
Maziarka, Łukasz, Majchrowski, Dawid, Danel, Tomasz, Gaiński, Piotr, Tabor, Jacek, Podolak, Igor, Morkisz, Paweł, Jastrzębski, Stanisław
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. Despite significant progress, non-pr
Externí odkaz:
http://arxiv.org/abs/2110.05841