37 citations · 58 across the 7 of their papers we have counts for
9 papers
nach0-pc: Multi-task Language Model with Molecular Point Cloud Encoder
Maksim Kuznetsov, Airat Valiev, Alex Aliper +4
Recent advancements have integrated Language Models (LMs) into a drug discovery pipeline. However, existing models mostly work with SMILES and SELFIES chemical string representatio…
BindGPT: A Scalable Framework for 3D Molecular Design via Language Modeling and Reinforcement Learning
Artem Zholus, Maksim Kuznetsov, Roman Schutski +4
Generating novel active molecules for a given protein is an extremely challenging task for generative models that requires an understanding of the complex physical interactions bet…
nach0: Multimodal Natural and Chemical Languages Foundation Model
Micha Livne, Zulfat Miftahutdinov, Elena Tutubalina +8
Large Language Models (LLMs) have substantially driven scientific progress in various domains, and many papers have demonstrated their ability to tackle complex problems with creat…
MolGrow: A Graph Normalizing Flow for Hierarchical Molecular Generation
Maksim Kuznetsov, Daniil Polykovskiy
We propose a hierarchical normalizing flow model for generating molecular graphs. The model produces new molecular structures from a single-node graph by recursively splitting ever…
Chemistry42: An AI-based platform for de novo molecular design
Yan A. Ivanenkov, Alex Zhebrak, Dmitry Bezrukov +7
Chemistry42 is a software platform for de novo small molecule design that integrates Artificial Intelligence (AI) techniques with computational and medicinal chemistry methods. Che…
Deterministic Decoding for Discrete Data in Variational Autoencoders
Daniil Polykovskiy, Dmitry Vetrov
Variational autoencoders are prominent generative models for modeling discrete data. However, with flexible decoders, they tend to ignore the latent codes. In this paper, we study…