GARLig: A Fully Automated Tool for Subset Selection of Large Fragment Spaces via a Self-Adaptive Genetic Algorithm

Abstract

In combinatorial chemistry, molecules are assembled according to combinatorial principles by linking suitable reagents or decorating a given scaffold with appropriate substituents from a large chemical space of starting materials. Often the number of possible combinations greatly exceeds the number feasible to handle by an in-depth in silico approach or even more if it should be experimentally synthesized. Therefore, powerful tools to efficiently enumerate large chemical spaces are required. They can be provided by genetic algorithms, which mimic Darwinian evolution. GARLig (genetic algorithm using reagents to compose ligands) has been developed to perform subset selection in large chemical compound spaces subject to target-specific 3D-scoring criteria. GARLig uses different scoring schemes, such as AutoDock4 Score, GOLDScore, and DrugScore(CSD), as fitness functions. Its genetic parameters have been optimized to characterize combinatorial libraries with respect to the binding to various targets of pharmaceutical interest. A large tripeptide library of 20(3) members has been used to profile amino acid frequencies in putative substrates for trypsin, thrombin, factor Xa, and plasmin. A peptidomimetic scaffold assembled from a selection of a 25(3) building block was used to test the performance of the evolutionary algorithm in suggesting potent inhibitors of the enzyme cathepsin D. In a final case study, our program was used to characterize and rank a combinatorial drug-like library comprising 33,750 potential thrombin inhibitors. These case studies demonstrate that GARLig finds experimentally confirmed potent leads by processing a significantly smaller subset of the fully enumerated combinatorial library. Furthermore, the profiles of amino acids computed by the genetic algorithm match the observed amino acid frequencies found by screening peptide libraries in substrate cleavage assays.

Optimization of a Genetic Algorithm for the Functionalization of Fullerenes

“…One such method is a Genetic Algorithm (GA). GAs have been broadly used for chemical search and minimization problems, including determination of cluster geometries, − protein folding, ligand docking, molecular design, and conformational searching of floppy molecules . A GA is an ‘intelligent’ stochastic search method that operates using the principles of Darwinian evolution.…”

Optimization of a genetic algorithm for searching molecular conformer space

“…GAs have been applied to various chemical problems including geometries of transition metal clusters, 25 geometries of molecular clusters, 26 ligand docking, 27 and molecular design. 28 A genetic algorithm begins with a randomly generated set (population) of genomes, each of which has an associated fitness score which is evaluated by some fitness function. The population of the next generation is generated by applying biological analog genetic operators such as random mutation and crossover (i.e., the synthesis of a new genome by matching complementary parts of two or more genomes).…”

Genetic Algorithm-Based Receptor Ligand: A Genetic Algorithm-Guided Generative Model to Boost the Novelty and Drug-Likeness of Molecules in a Sampling Chemical Space

“…On the other hand, structure-based methods, such as LUDI, PROLIGAND, SPROUT, CONCERTS, and scaffold hopping, , create novel small molecules by incrementally adding, deleting, inserting, or replacing fragments for a chemical scaffold embedded in the binding pocket of a target. Most of these traditional methods adopt the genetic algorithms (GA) as the evolution strategy − for structural exploration.…”