330 citations · 2.7k across the 74 of their papers we have counts for
10 papers · 1 filter
ShEPhERD: Diffusing shape, electrostatics, and pharmacophores for bioisosteric drug design
Keir Adams, Kento Abeywardane, Jenna Fromer +1
Engineering molecules to exhibit precise 3D intermolecular interactions with their environment forms the basis of chemical design. In ligand-based drug design, bioisosteric analogu…
Generative Artificial Intelligence for Navigating Synthesizable Chemical Space
Wenhao Gao, Shitong Luo, Connor W. Coley
We introduce SynFormer, a generative modeling framework designed to efficiently explore and navigate synthesizable chemical space. Unlike traditional molecular generation approache…
Batched Bayesian optimization by maximizing the probability of including the optimum
Jenna Fromer, Runzhong Wang, Mrunali Manjrekar +3
Batched Bayesian optimization (BO) can accelerate molecular design by efficiently identifying top-performing compounds from a large chemical library. Existing acquisition strategie…
Procedural Synthesis of Synthesizable Molecules
Michael Sun, Alston Lo, Minghao Guo +3
Designing synthetically accessible molecules and recommending analogs to unsynthesizable molecules are important problems for accelerating molecular discovery. We reconceptualize b…
Double-Ended Synthesis Planning with Goal-Constrained Bidirectional Search
Kevin Yu, Jihye Roh, Ziang Li +3
Computer-aided synthesis planning (CASP) algorithms have demonstrated expert-level abilities in planning retrosynthetic routes to molecules of low to moderate complexity. However,…
Projecting Molecules into Synthesizable Chemical Spaces
Shitong Luo, Wenhao Gao, Zuofan Wu +3
Discovering new drug molecules is a pivotal yet challenging process due to the near-infinitely large chemical space and notorious demands on time and resources. Numerous generative…
OpenChemIE: An Information Extraction Toolkit For Chemistry Literature
Vincent Fan, Yujie Qian, Alex Wang +3
Information extraction from chemistry literature is vital for constructing up-to-date reaction databases for data-driven chemistry. Complete extraction requires combining informati…
Beyond Major Product Prediction: Reproducing Reaction Mechanisms with Machine Learning Models Trained on a Large-Scale Mechanistic Dataset
Joonyoung F. Joung, Mun Hong Fong, Jihye Roh +3
Mechanistic understanding of organic reactions can facilitate reaction development, impurity prediction, and in principle, reaction discovery. While several machine learning models…
Effective Protein-Protein Interaction Exploration with PPIretrieval
Chenqing Hua, Connor Coley, Guy Wolf +2
Protein-protein interactions (PPIs) are crucial in regulating numerous cellular functions, including signal transduction, transportation, and immune defense. As the accuracy of mul…
Revealing the Relationship Between Publication Bias and Chemical Reactivity with Contrastive Learning
Wenhao Gao, Priyanka Raghavan, Ron Shprints +1
A synthetic method's substrate tolerance and generality are often showcased in a "substrate scope" table. However, substrate selection exhibits a frequently discussed publication b…