What is Chemoinformatics?

Introduction

Chemoinformatic approaches that combine chemistry with programming are now forming an integral part of the drug discovery pipeline. Algorithms can make predictions for target discovery, toxicity, and binding efficacy, among others, before doing lab testing. While not a substitute for lab experiments, chemoinformatic approaches are useful as they allow screening of an enormous chemical space, reducing costs and aiding in hypothesis generation (Faulon et al, p155). To begin a discussion of chemoinformatics, we need to introduce some fundamental concepts.

Algorithms are recipes or a series of steps that need to be taken to complete a task. Traditional algorithms rely on a set of pre-set rules programmed by a technician. The first type of algorithm we will use is called a genetic algorithm. A genetic algorithm takes a structure and randomly mutates it to select the structures for a desired property. Then the new mutated molecule is added to the valid list of children in a loop to generate more molecules.

Machine learning algorithms are a class of algorithms that detect patterns in data to derive rules for making predictions. Machine learning algorithms do not require programming of pre-set rules. Instead, these algorithms learn as they process…