no code implementations • 20 Oct 2023 • Benson Chen, Mohammad M. Sultan, Theofanis Karaletsos
DNA-Encoded Library (DEL) has proven to be a powerful tool that utilizes combinatorially constructed small molecules to facilitate highly-efficient screening assays.
1 code implementation • 30 Nov 2022 • Kirill Shmilovich, Benson Chen, Theofanis Karaletsos, Mohammad M. Sultan
Computational models have been deployed to learn the latent binding affinities that are correlated to the sequenced count data; however, this correlation is often obfuscated by various sources of noise introduced in its complicated data-generation process.
no code implementations • NeurIPS Workshop AI4Scien 2021 • Benson Chen, Xiang Fu, Regina Barzilay, Tommi Jaakkola
Equipped with the learned fragment vocabulary, we propose Fragment-based Sequential Translation (FaST), which learns a reinforcement learning (RL) policy to iteratively translate model-discovered molecules into increasingly novel molecules while satisfying desired properties.
2 code implementations • 8 Jun 2020 • Benson Chen, Gary Bécigneul, Octavian-Eugen Ganea, Regina Barzilay, Tommi Jaakkola
Current graph neural network (GNN) architectures naively average or sum node embeddings into an aggregated graph representation -- potentially losing structural or semantic information.
Ranked #1 on Graph Regression on Lipophilicity (using extra training data)
no code implementations • 21 Oct 2019 • Benson Chen, Tianxiao Shen, Tommi S. Jaakkola, Regina Barzilay
We propose a new model for making generalizable and diverse retrosynthetic reaction predictions.
2 code implementations • 29 May 2019 • Benson Chen, Regina Barzilay, Tommi Jaakkola
Much of the recent work on learning molecular representations has been based on Graph Convolution Networks (GCN).
no code implementations • ICLR 2018 • Benson Chen, Connor Coley, Regina Barzilay, Tommi Jaakkola
Deep learning algorithms are increasingly used in modeling chemical processes.