Reflections on improving drug success rates with AIDD and CADD
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摘要:
随着人工智能(AI)和计算科学的迅速发展,特别是人工智能药物设计(AIDD)与计算机辅助药物设计(CADD)技术的引入,自然语言处理、图像识别、深度学习和机器学习等多种技术为新药开发提供了革命性的新途径,大幅提升了研发流程的效率和成功率。在药物发现过程中,AI技术加速了药物靶点的识别、候选药物的筛选、药理评估及质量检验,有效降低了研发风险和成本。本文深入探讨AIDD和CADD技术在药物研发中的应用,分析它们在提升药物设计成功率和药物研发效率方面的思考与探索,并探讨这些技术的未来发展趋势及可能面临的挑战。
Abstract:The rapid advancements in artificial intelligence (AI) and computational sciences, particularly through the introduction of artificial intelligence drug design (AIDD) and computer-aided drug design (CADD) technologies, have revolutionized pathways in drug development. These include techniques such as natural language processing, image recognition, deep learning, and machine learning. By employing advanced algorithms and data processing techniques, these technologies have significantly enhanced the efficiency and success rate of R&D processes. In drug discovery, AI technologies have accelerated the identification of drug targets, screening of candidate drugs, pharmacological assessments, and quality control, effectively reducing R&D risks and costs. This article delves into the application of AIDD and CADD in drug development, analyzing their roles in enhancing the success rates and efficiencies of drug design, exploring their future trends, and addressing the potential challenges.
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表 1 药效团中药效特征元素[18]
特征元素 药效团内容 氢键受体 sp2或sp3杂化的氧原子/与碳原子以双键形式相连的S原子/与碳原子以双键或者三键相连的氮原子 氢键供体 氢原子以及与之相连的氧原子和氮原子 疏水中心 只要和不带电原子或电负性中心相连的一组连续的碳原子都可以形成疏水中心 电荷中心 与受体形成盐桥或较强的静电相互作用,带有电荷的原子/在生理pH下会发生电离的中性基团 芳环中心 形成π-π相互作用,五元或六元芳环 表 2 按照药物设计理念分类的分子生成方法
种 类 分子生成方法 经典方法 基于规则的方法 递归神经网络 生成对抗网络 变分自编码器 强化学习 融入药学思想的生成模型 基于受体结构的生成模型 基于配体结构的生成模型 基于药效团的生成模型 基于片段的生成模型 其他 基于谱学的生成模型 基于化学反应的生成模型 表 3 经筛选得到的5-HT1A受体小分子活性数据
Hit 相对分子质量 氢键受体 氢键供体 ALogP [3H]-8-OH-DPAT Ki/(nmol/L) [35S]GTPyS EC50/(nmol/L) FW01 476.6 5 1 5.84 51.9±16.4 7 FW02 347.5 4 2 3.35 70.5±14.8 77 FW03 350.4 4 1 4.04 96.5±13.1 404 FW04 394.5 5 2 3.77 103.5±23.4 128 FW05 429.0 4 1 6.10 123.6±32.0 534 FW06 294.3 4 3 3.27 133.4±9.7 434 FW07 397.5 4 1 4.60 225.2±20.2 340 FW08 344.8 4 2 4.20 294.9±5.0 572 FW09 446.0 5 1 4.74 320.1±76.9 597 FW10 396.9 4 1 4.27 351.7±19.0 ND 5-羟色胺 1.8±0.1 3 -
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