Nuisance small molecules under a machine-learning lens

Transferable Atomic Multipole Machine Learning Models for Small Organic Molecules.

Accurate representation of the molecular electrostatic potential, which is often expanded in distributed multipole moments, is crucial for an efficient evaluation of intermolecular interactions. Here we introduce a machine learning model for multipole coefficients of atom types H, C, O, N, S, F, and Cl in any molecular conformation. The model is trained on quantum-chemical results for atoms in ...

ToxiM: A Toxicity Prediction Tool for Small Molecules Developed Using Machine Learning and Chemoinformatics Approaches

The experimental methods for the prediction of molecular toxicity are tedious and time-consuming tasks. Thus, the computational approaches could be used to develop alternative methods for toxicity prediction. We have developed a tool for the prediction of molecular toxicity along with the aqueous solubility and permeability of any molecule/metabolite. Using a comprehensive and curated set of to...

INTERACTION OF PROTEINS WITH SMALL MOLECULES

Machine learning and small robot navigation

Developing successful na\ igation and mapping strategies is an essential part of autonomous robot research. However, hardware limitations often make for inaccurate systems. This project serves to investigate efficient alternatives to mapping an environment, by ftrst creating a mobile robot, and then applying machine learning to the robot and controlling systems to increase the robustness of the...

Tupleware: Distributed Machine Learning on Small Clusters

There is a fundamental discrepancy between the targeted and actual users of current analytics frameworks. Most systems are designed for the challenges of the Googles and Facebooks of the world— petabytes of data distributed across large cloud deployments consisting of thousands of cheap commodity machines. Yet, the vast majority of users operate clusters ranging from a few to a few dozen nodes,...