Search Results for author: Piotr Gaiński

Found 7 papers, 6 papers with code

Chimera: Accurate retrosynthesis prediction by ensembling models with diverse inductive biases

no code implementations6 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.

Retrosynthesis

RetroGFN: Diverse and Feasible Retrosynthesis using GFlowNets

2 code implementations26 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.

Retrosynthesis Single-step retrosynthesis

RGFN: Synthesizable Molecular Generation Using GFlowNets

1 code implementation1 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.

Re-evaluating Retrosynthesis Algorithms with Syntheseus

1 code implementation30 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.

Benchmarking Multi-step retrosynthesis +1

ChiENN: Embracing Molecular Chirality with Graph Neural Networks

1 code implementation5 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.

Drug Discovery Molecular Property Prediction +1

Step by Step Loss Goes Very Far: Multi-Step Quantization for Adversarial Text Attacks

1 code implementation10 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.

Adversarial Text Quantization

Relative Molecule Self-Attention Transformer

1 code implementation12 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.

Drug Discovery Property Prediction +1

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