Dipartimento di Farmacia-Scienze del Farmaco, Università degli Studi di Bari "Aldo Moro", Via E. Orabona, 4, I-70126 Bari, Italy.
Molecular Horizon srl, Via Montelino 32, 06084 Bettona, Italy.
J Chem Inf Model. 2020 Oct 26;60(10):4582-4593. doi: 10.1021/acs.jcim.0c00517. Epub 2020 Sep 9.
Artificial intelligence and multiobjective optimization represent promising solutions to bridge chemical and biological landscapes by addressing the automated design of compounds as a result of a humanlike creative process. In the present study, we conceived a novel pair-based multiobjective approach implemented in an adapted SMILES generative algorithm based on recurrent neural networks for the automated design of new molecules whose overall features are optimized by finding the best trade-offs among relevant physicochemical properties (MW, logP, HBA, HBD) and additional similarity-based constraints biasing specific biological targets. In this respect, we carried out the design of chemical libraries targeting neuraminidase, acetylcholinesterase, and the main protease of severe acute respiratory syndrome coronavirus 2. Several quality metrics were employed to assess drug-likeness, chemical feasibility, diversity content, and validity. Molecular docking was finally carried out to better evaluate the scoring and posing of the generated molecules with respect to X-ray cognate ligands of the corresponding molecular counterparts. Our results indicate that artificial intelligence and multiobjective optimization allow us to capture the latent links joining chemical and biological aspects, thus providing easy-to-use options for customizable design strategies, which are especially effective for both lead generation and lead optimization. The algorithm is freely downloadable at https://github.com/alberdom88/moo-denovo and all of the data are available as Supporting Information.
人工智能和多目标优化代表了有前途的解决方案，可以通过解决化合物的自动化设计问题来弥合化学和生物学领域之间的差距，而化合物的自动化设计是人类创造性过程的结果。在本研究中，我们设计了一种新颖的基于对的多目标方法，该方法在基于递归神经网络的适应性 SMILES 生成算法中实现，用于自动化设计新分子，通过在相关物理化学性质（MW、logP、HBA、HBD）和附加基于相似性的约束之间找到最佳折衷方案，优化新分子的整体特征，从而偏向特定的生物靶标。在这方面，我们针对神经氨酸酶、乙酰胆碱酯酶和严重急性呼吸综合征冠状病毒 2 的主要蛋白酶进行了化学文库的设计。采用了多种质量指标来评估药物相似性、化学可行性、多样性含量和有效性。最后进行了分子对接，以更好地评估生成分子与相应分子对应物的 X 射线同源配体的评分和构象。我们的结果表明，人工智能和多目标优化使我们能够捕捉连接化学和生物学方面的潜在联系，从而为可定制的设计策略提供易于使用的选择，这些策略对于先导化合物的生成和优化都特别有效。该算法可在 https://github.com/alberdom88/moo-denovo 上免费下载，所有数据均可作为支持信息获得。
