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SmartCADD:具有可解释性的 AI-QM 赋能药物发现平台。

SmartCADD: AI-QM Empowered Drug Discovery Platform with Explainability.

机构信息

Department of Chemistry, Southern Methodist University, Dallas, Texas 75205, United States.

Department of Computer Science, Southern Methodist University, Dallas, Texas 75205, United States.

出版信息

J Chem Inf Model. 2024 Sep 9;64(17):6799-6813. doi: 10.1021/acs.jcim.4c00720. Epub 2024 Aug 23.

DOI:10.1021/acs.jcim.4c00720
PMID:39177478
Abstract

Artificial intelligence (AI) has emerged as a pivotal force in enhancing productivity across various sectors, with its impact being profoundly felt within the pharmaceutical and biotechnology domains. Despite AI's rapid adoption, its integration into scientific research faces resistance due to myriad challenges: the opaqueness of AI models, the intricate nature of their implementation, and the issue of data scarcity. In response to these impediments, we introduce SmartCADD, an innovative, open-source virtual screening platform that combines deep learning, computer-aided drug design (CADD), and quantum mechanics methodologies within a user-friendly Python framework. SmartCADD is engineered to streamline the construction of comprehensive virtual screening workflows that incorporate a variety of formerly independent techniques─spanning ADMET property predictions, de novo 2D and 3D pharmacophore modeling, molecular docking, to the integration of explainable AI mechanisms. This manuscript highlights the foundational principles, key functionalities, and the unique integrative approach of SmartCADD. Furthermore, we demonstrate its efficacy through a case study focused on the identification of promising lead compounds for HIV inhibition. By democratizing access to advanced AI and quantum mechanics tools, SmartCADD stands as a catalyst for progress in pharmaceutical research and development, heralding a new era of innovation and efficiency.

摘要

人工智能(AI)已成为提高各行业生产力的关键力量,其影响在制药和生物技术领域尤为显著。尽管 AI 得到了迅速采用,但由于其模型的不透明性、实施的复杂性以及数据匮乏等诸多挑战,其在科学研究中的整合仍面临阻力。针对这些障碍,我们引入了 SmartCADD,这是一个创新的、开源的虚拟筛选平台,它在用户友好的 Python 框架内结合了深度学习、计算机辅助药物设计(CADD)和量子力学方法。SmartCADD 的设计目的是简化构建全面的虚拟筛选工作流程,该流程整合了各种以前独立的技术——涵盖 ADMET 性质预测、从头 2D 和 3D 药效团建模、分子对接,以及可解释 AI 机制的集成。本文重点介绍了 SmartCADD 的基础原理、关键功能和独特的集成方法。此外,我们通过一项专注于识别有希望的 HIV 抑制先导化合物的案例研究来展示其功效。通过使先进的 AI 和量子力学工具民主化,SmartCADD 成为推动药物研发进步的催化剂,开创了创新和效率的新时代。

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引用本文的文献

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Quantum Mechanics in Drug Discovery: A Comprehensive Review of Methods, Applications, and Future Directions.药物发现中的量子力学:方法、应用及未来方向的全面综述
Int J Mol Sci. 2025 Jun 30;26(13):6325. doi: 10.3390/ijms26136325.
2
Strategies for robust, accurate, and generalizable benchmarking of drug discovery platforms.药物发现平台稳健、准确且可推广的基准测试策略。
bioRxiv. 2024 Dec 16:2024.12.10.627863. doi: 10.1101/2024.12.10.627863.