Abstract

Accurate protein-ligand binding poses are the prerequisites of structure-based binding affinity prediction, and also provide the structural basis for in depth lead optimization in small molecule drug design. Ligand-based modeling approaches primarily extract valuable information from the structural features of small molecules to assess their potential as drug candidates against specific targets. However, it is challenging to provide reasonable predictions of binding poses for different molecules, due to the complexity and diversity of the chemical space of small molecules. Similarity-based molecular alignment techniques can effectively narrow the search range, as structurally similar molecules are likely to have similar binding modes, with higher similarity usually correlating to higher success rates. However, molecular similarity isnt consistently high because molecules often require changes to achieve specific purposes, leading to reduced alignment precision. To address this issue, we propose a new alignment method--Z-align. This method uses topological structural information as a criterion for evaluating similarity, reducing the reliance on molecular fingerprint similarity. Our method has achieved significantly higher success rates than other methods at moderate levels of similarity. Additionally, our approach can comprehensively and flexibly optimize bond lengths and angles of molecules, maintaining high accuracy even when dealing with larger molecules. Consequently, our proposed solution helps in achieving more accurate binding poses in protein-ligand docking problems, facilitating the development of small molecule drugs.