(683b) Chemlg - a Smart and Massively Parallel Code to Accelerate the Molecular Library Generation
AIChE Annual Meeting
2018 AIChE Annual Meeting
Computational Molecular Science and Engineering Forum
Software Engineering in and for the Molecular Sciences
Thursday, November 1, 2018 - 1:00pm to 1:15pm
Our work aims to extend and generalize library generation to identify molecular lead candidates and reaction networks in various other applications such as functional polymers, optoelectronics, and catalysis. Our massively parallel generator ChemLG is part of our ChemHTPS program suite for automated, virtual high-throughput screening studies, and it offers a multitude of options to customize and restrict the scope of the enumerated chemical space and thus tailor it for the demands of specific applications. To streamline the non-combinatorial exploration of chemical space, we incorporate genetic algorithms into the framework. Genetic algorithms have shown to be effective in optimizing chemical structures and generating useful compounds for different target applications. We built the code in python and implement parallelization using mpi4py library. In addition to implementing smarter algorithms, we also focus on the ease of use, workflow, and code integration to make this technology more accessible to the community.