2022年9月7日，望石团队在JCIM上发表了“Modified Electrostatic Complementary Score Function and Its Application Boundary Exploration in Drug Design” 相关成果。该成果利用深度学习介入，提出新的静电势互补函数计算，并引入了两种新的参数约束条件，在控制计算成本前提下，得到了计算精度接近量化计算的快速高精准模型。该模型中函数的预测精度远超行业内常用函数，且可直接应用于分子活性相关的药物研发场景中。
静电相互作用作为关键的强非共价相互作用（NCI），是药物设计公认的一个重要组成部分。通过匹配靶点口袋和配体的3D势能面，我们仅能定性的估计相关强度。若通过计算获得定量结果，通常需要通过量子化学方法如密度泛函理论（DFT）计算或分子力场（MMFF）的方法。高精度的量化计算结果近似于湿试验方法得到的结果，但计算成本极其高昂；而分子力场精准度不足。为了平衡计算精度和成本，AI+静电势互补函数成为近几年业内努力方向。业内已披露的解决方案，普遍存在函数静电势互补和结构互补结果一致性低和结果与应用场景关联度不高的问题。
DOI: 10.1021/acs.jcim.2c00616
望石智慧官方解读：https://mp.weixin.qq.com/s/7gx1w5BlLYfYHI-0xUVF_g
附论文摘要：
In recent years, machine learning (ML) models have been found to quickly predict various molecular properties with accuracy comparable to high-level quantum chemistry methods. One such example is the calculation of electrostatic potential (ESP). Different ESP prediction ML models were proposed to generate surface molecular charge distribution. Electrostatic complementarity (EC) can apply ESP data to quantify the complementarity between a ligand and its binding pocket, leading to the potential to increase the efficiency of drug design. However, there is not much research discussing EC score functions and their applicability domain. We propose a new EC score function modified from the one originally developed by Bauer and Mackey, and confirm its effectiveness against the available Pearson’s R correlation coefficient. Additionally, the applicability domain of the EC score and two indices used to define the EC score application scope will be discussed.