An optimization algorithm for conformer generation based on the bond contribution ranking.

Many works in computational drug discovery require the conformer generation of small molecules. Most existing tools aim to generate diverse conformers and deal with all of the rotatable bonds without distinction. There are some problems in existing approaches, such as the combinatorial explosion of conformers along with the number of rotatable bonds increasing and the incomplete sampling of the conformational space. Here, we present an optimized conformer generation algorithm based on the bond contribution ranking (ABCR) to find the optimal conformer under any specified scoring function. Compared with existing methods, our method can improve molecular conformational searching and protein-ligand docking performance. Meanwhile, our method has the same or broader coverage of conformational space in the global conformer sampling. Our research shows it can achieve the optima with small numbers of generated conformers and small numbers of iterations.