• Publications
  • Influence
MoleculeNet: A Benchmark for Molecular Machine Learning
TLDR
MoleculeNet benchmarks demonstrate that learnable representations are powerful tools for molecular machine learning and broadly offer the best performance, however, this result comes with caveats. Expand
Graph Convolutional Policy Network for Goal-Directed Molecular Graph Generation
TLDR
Graph Convolutional Policy Network (GCPN) is proposed, a general graph convolutional network based model for goal-directed graph generation through reinforcement learning that can achieve 61% improvement on chemical property optimization over state-of-the-art baselines while resembling known molecules, and achieve 184% improved on the constrained property optimization task. Expand
Random-coil behavior and the dimensions of chemically unfolded proteins.
TLDR
It appears that the mean dimensions of the large majority of chemically denatured proteins are effectively indistinguishable from themean dimensions of a random-coil ensemble. Expand
Molecular graph convolutions: moving beyond fingerprints
TLDR
Molecular graph convolutions are described, a machine learning architecture for learning from undirected graphs, specifically small molecules, that represent a new paradigm in ligand-based virtual screening with exciting opportunities for future improvement. Expand
Strategies for Pre-training Graph Neural Networks
TLDR
A new strategy and self-supervised methods for pre-training Graph Neural Networks (GNNs) that avoids negative transfer and improves generalization significantly across downstream tasks, leading up to 9.4% absolute improvements in ROC-AUC over non-pre-trained models and achieving state-of-the-art performance for molecular property prediction and protein function prediction. Expand
Exploring the helix-coil transition via all-atom equilibrium ensemble simulations.
TLDR
The ensemble folding of two 21-residue alpha-helical peptides has been studied using all-atom simulations under several variants of the AMBER potential in explicit solvent and extensive sampling results in complete convergence to ensemble equilibrium. Expand
MDTraj: A Modern Open Library for the Analysis of Molecular Dynamics Trajectories.
TLDR
MDTraj is a modern, lightweight, and fast software package for analyzing MD simulations that simplifies the analysis of MD data and connects these datasets with the modern interactive data science software ecosystem in Python. Expand
CHARMM-GUI Input Generator for NAMD, GROMACS, AMBER, OpenMM, and CHARMM/OpenMM Simulations Using the CHARMM36 Additive Force Field
  • Jumin Lee, Xi Cheng, +14 authors W. Im
  • Chemistry, Medicine
  • Journal of chemical theory and computation
  • 12 November 2015
TLDR
The optimal simulation protocol for each program has been implemented in CHARMM-GUI and is expected to be applicable to the remainder of the additive C36 FF including the proteins, nucleic acids, carbohydrates, and small molecules. Expand
OpenMM 7: Rapid development of high performance algorithms for molecular dynamics
TLDR
OpenMM is a molecular dynamics simulation toolkit with a unique focus on extensibility, which makes it an ideal tool for researchers developing new simulation methods, and also allows those new methods to be immediately available to the larger community. Expand
MoleculeNet: a benchmark for molecular machine learning† †Electronic supplementary information (ESI) available. See DOI: 10.1039/c7sc02664a
A large scale benchmark for molecular machine learning consisting of multiple public datasets, metrics, featurizations and learning algorithms.
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