Connor W. Coley

DeepAI AI Chat

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research

∙ 05/19/2023

RxnScribe: A Sequence Generation Model for Reaction Diagram Parsing

Reaction diagram parsing is the task of extracting reaction schemes from...

0 Yujie Qian, et al. ∙

research

∙ 03/11/2023

Prefix-tree Decoding for Predicting Mass Spectra from Molecules

Computational predictions of mass spectra from molecules have enabled th...

10 Samuel Goldman, et al. ∙

research

∙ 11/28/2022

Reinforced Genetic Algorithm for Structure-based Drug Design

Structure-based drug design (SBDD) aims to discover drug candidates by f...

64 Tianfan Fu, et al. ∙

research

∙ 11/04/2022

De novo PROTAC design using graph-based deep generative models

PROteolysis TArgeting Chimeras (PROTACs) are an emerging therapeutic mod...

1 Divya Nori, et al. ∙

research

∙ 10/13/2022

Computer-Aided Multi-Objective Optimization in Small Molecule Discovery

Molecular discovery is a multi-objective optimization problem that requi...

13 Jenna C. Fromer, et al. ∙

research

∙ 06/22/2022

Sample Efficiency Matters: A Benchmark for Practical Molecular Optimization

Molecular optimization is a fundamental goal in the chemical sciences an...

0 Wenhao Gao, et al. ∙

research

∙ 05/28/2022

Robust Molecular Image Recognition: A Graph Generation Approach

Molecular image recognition is a fundamental task in information extract...

4 Yujie Qian, et al. ∙

research

∙ 05/17/2022

A graph representation of molecular ensembles for polymer property prediction

Synthetic polymers are versatile and widely used materials. Similar to s...

11 Matteo Aldeghi, et al. ∙

research

∙ 05/03/2022

Self-focusing virtual screening with active design space pruning

High-throughput virtual screening is an indispensable technique utilized...

3 David E. Graff, et al. ∙

research

∙ 12/09/2021

Bringing Atomistic Deep Learning to Prime Time

Artificial intelligence has not yet revolutionized the design of materia...

21 Nathan C. Frey, et al. ∙

research

∙ 12/06/2021

Scalable Geometric Deep Learning on Molecular Graphs

Deep learning in molecular and materials sciences is limited by the lack...

3 Nathan C. Frey, et al. ∙

research

∙ 10/19/2021

Permutation invariant graph-to-sequence model for template-free retrosynthesis and reaction prediction

Synthesis planning and reaction outcome prediction are two fundamental p...

16 Zhengkai Tu, et al. ∙

research

∙ 10/12/2021

Amortized Tree Generation for Bottom-up Synthesis Planning and Synthesizable Molecular Design

Molecular design and synthesis planning are two critical steps in the pr...

5 Wenhao Gao, et al. ∙

research

∙ 10/08/2021

Learning 3D Representations of Molecular Chirality with Invariance to Bond Rotations

Molecular chirality, a form of stereochemistry most often describing rel...

4 Keir Adams, et al. ∙

research

∙ 09/22/2021

Differentiable Scaffolding Tree for Molecular Optimization

The structural design of functional molecules, also called molecular opt...

35 Tianfan Fu, et al. ∙

research

∙ 09/08/2021

Machine learning modeling of family wide enzyme-substrate specificity screens

Biocatalysis is a promising approach to sustainably synthesize pharmaceu...

28 Samuel Goldman, et al. ∙

research

∙ 08/27/2021

Machine learning on DNA-encoded library count data using an uncertainty-aware probabilistic loss function

DNA-encoded library (DEL) screening and quantitative structure-activity ...

6 Katherine S. Lim, et al. ∙

research

∙ 05/26/2021

BioNavi-NP: Biosynthesis Navigator for Natural Products

Nature, a synthetic master, creates more than 300,000 natural products (...

1 Shuangjia Zheng, et al. ∙

research

∙ 12/13/2020

Accelerating high-throughput virtual screening through molecular pool-based active learning

Structure-based virtual screening is an important tool in early stage dr...

8 David E. Graff, et al. ∙

research

∙ 06/12/2020

Learning Graph Models for Template-Free Retrosynthesis

Retrosynthesis prediction is a fundamental problem in organic synthesis,...

17 Vignesh Ram Somnath, et al. ∙

research

∙ 05/20/2020

Uncertainty Quantification Using Neural Networks for Molecular Property Prediction

Uncertainty quantification (UQ) is an important component of molecular p...

25 Lior Hirschfeld, et al. ∙

research

∙ 04/26/2020

Learning To Navigate The Synthetically Accessible Chemical Space Using Reinforcement Learning

Over the last decade, there has been significant progress in the field o...

4 Sai Krishna Gottipati, et al. ∙

research

∙ 03/30/2020

Autonomous discovery in the chemical sciences part II: Outlook

This two-part review examines how automation has contributed to differen...

7 Connor W. Coley, et al. ∙

research

∙ 03/30/2020

Autonomous discovery in the chemical sciences part I: Progress

This two-part review examines how automation has contributed to differen...

23 Connor W. Coley, et al. ∙

research

∙ 02/17/2020

The Synthesizability of Molecules Proposed by Generative Models

The discovery of functional molecules is an expensive and time-consuming...

83 Wenhao Gao, et al. ∙

research

∙ 01/06/2020

Retrosynthesis Prediction with Conditional Graph Logic Network

Retrosynthesis is one of the fundamental problems in organic chemistry. ...

6 Hanjun Dai, et al. ∙

research

∙ 01/19/2019

Learning retrosynthetic planning through self-play

The problem of retrosynthetic planning can be framed as one player game,...

0 John S. Schreck, et al. ∙

research

∙ 09/13/2017

Predicting Organic Reaction Outcomes with Weisfeiler-Lehman Network

The prediction of organic reaction outcomes is a fundamental problem in ...

0 Wengong Jin, et al. ∙