Lead optimization in drug discovery is a challenging process that heavily relies on hypotheses and the experience of medicinal chemists. This often leads to uncertain outcomes and inefficiency. Furthermore, the process is time-consuming and requires significant resources. Therefore, the introduction of artificial intelligence (AI) predictive tools to accelerate this process would be highly valuable in the field of drug discovery.
Silico methods such as free energy perturbation (FEP) and molecular mechanics generalized born surface area (MM-GB/SA) have proven useful in lead optimization by calculating binding free energy. However, their complex preparation process, limited molecule throughput, and restricted allowance for changes between molecules hinder their routine usage. There is an urgent need to develop an efficient and accurate in silico predictive tool to guide lead optimization.
In a study published in Nature Computational Science, a team of researchers led by Prof. Zheng Mingyue from the Shanghai Institute of Materia Medica (SIMM) of the Chinese Academy of Sciences developed a pairwise binding comparison network (PBCNet).
This network predicts the relative binding affinity among congeneric ligands by using a physics-informed graph attention mechanism with a pair of protein pocket-ligand complexes as input. PBCNet demonstrates practical value in guiding structure-based drug lead optimization with its speed, precision, and ease-of-use.
To validate the performance of PBCNet in terms of ranking ability and accuracy, Zheng’s group