Neural network methods for identification and optimization of quantum mechanical features needed for bioactivity.

Abstract

This paper presents a new approach to the discovery and design of bioactive compounds. The focus of this application will be on the analysis of enzymatic inhibitors. At present the discovery of enzymatic inhibitors for therapeutic use is often accomplished through random searches. The first phase of discovery is a random search through a large pre-fabricated chemical library. Many molecules are tested with refined enzyme for signs of inhibition. Once a group of lead compounds have been discovered the chemical intuition of biochemists is used to find structurally related compounds that are more effective. This step requires new molecules to be conceived and synthesized, and it is the most time-consuming and expensive step. The development of computational and theoretical methods for prediction of the molecular structure that would bind most tightly prior to synthesis and testing, would facilitate the design of novel inhibitors. In the past, our work has focused on solving the problem of predicting the bioactivity of a molecule prior to synthesis. We used a neural network trained with the bioactivity of known compounds to predict the bioactivity of unknown compounds. In our current work, we use a separate neural network in conjunction with a trained neural network in an attempt to gain insight as to how to modify existing compounds and increase their bioactivity.