Leland Stanford Junior University

Memorial Sloan Kettering Cancer Center

Vijay S Pande

Evan N Feinberg

Bharath Ramsundar

Gavril W Pasternak

Susruta Majumdar

Imran Haque

Systems and methods for spatial graph convolutions in accordance with embodiments of the invention are illustrated. One embodiment includes a method for predicting characteristics for molecules, wherein the method includes performing a first set of graph convolutions with a spatial graph representation of a set of molecules, wherein the first set of graph convolutions are based on bonds between the set of molecules, performing a second set of graph convolutions with the spatial graph representation, wherein the second set of graph convolutions are based on at least a distance between each atom and other atoms of the set of molecules, performing a graph gather with the spatial graph representation to produce a feature vector, and predicting a set of one or more characteristics for the set of molecules based on the feature vector.

Systems and methods for spatial graph convolutions with applications to drug discovery and molecular simulation

Systems and methods for molecular simulation in accordance with embodiments of the invention are illustrated. One embodiment includes a method for predicting a relationship between a ligand and a receptor. The method includes steps for identifying a plurality of conformations of a receptor, computing docking scores for each of the plurality of conformations and a set of one or more ligands, and predicting a relationship between the set of one or more ligands and the plurality of conformations of the receptor.

Machine learning and molecular simulation based methods for enhancing binding and activity prediction

Ligands of the mu, kappa, and delta opioid receptors and methods of using them are disclosed. In particular, the invention relates to the discovery of new opioid receptor ligands based on molecular dynamics simulations of conformational states of the &#x3bc; opioid receptor and the use of the identified opioid receptor ligands as analgesics, anti-diarrheal agents, and overdose reversal agents.

Ligands of the mu, kappa, and delta opioid receptors

Methods and algorithms are presented that implement linear algebraic techniques for rapidly estimating chemical similarities for several popular measures. The methods of the present invention reflect source similarity measures for both Tanimoto calculation and rank ordering. After a precalculation step on a database, the methods of the present invention afford several orders of magnitude of speedup in database screening. The present invention also provides an asymptotic speedup for large similarity matrix construction problems, reducing the number of conventional slow similarity evaluations required from quadratic to linear scaling.

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Menlo Park, CA, United States of America

Vijay S Pande