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在人类可成药基因组中,仍有相当大一部分尚未被小分子治疗药物靶向。随着AlphaFold等蛋白结构预测技术的突破,全基因组药物发现正逐渐成为现实。然而,现有的虚拟筛选工具远不能满足这一需求。无论是传统的分子对接方法,还是近年来发展的深度学习方法,其计算成本都过于高昂,难以覆盖基因组尺度的靶点筛选。

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(图片来源:《Science》)

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药物筛选平台首次实现人类基因组级靶点全覆盖
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药物筛选平台首次实现人类基因组级靶点全覆盖
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在人类可成药基因组中,仍有相当大一部分尚未被小分子治疗药物靶向。随着AlphaFold等蛋白结构预测技术的突破,全基因组药物发现正逐渐成为现实。然而,现有的虚拟筛选工具远不能满足这一需求。无论是传统的分子对接方法,还是近年来发展的深度学习方法,其计算成本都过于高昂,难以覆盖基因组尺度的靶点筛选。

. 药物筛选平台首次实现人类基因组级靶点全覆盖. 科技导报, 2026 , 44 (2) : 9 -9 .
. Science & Technology Review, 2026 , 44 (2) : 9 -9 .
在人类可成药基因组中,仍有相当大一部分尚未被小分子治疗药物靶向。随着AlphaFold等蛋白结构预测技术的突破,全基因组药物发现正逐渐成为现实。然而,现有的虚拟筛选工具远不能满足这一需求。无论是传统的分子对接方法,还是近年来发展的深度学习方法,其计算成本都过于高昂,难以覆盖基因组尺度的靶点筛选。
清华大学智能产业研究院兰艳艳团队联合生命学院、化学系团队创新研发人工智能(AI)驱动的超高通量药物虚拟筛选平台DrugCLIP,筛选速度对比传统方法实现了百万倍提升,同时在预测准确率上也取得显著突破。2026年1月9日,相关研究成果发表于《Science》。
DrugCLIP是一种用于快速、精准虚拟筛选的对比学习框架。该方法将蛋白结合口袋与小分子编码到共享的潜在表示空间中,训练过程同时利用了大规模合成数据以及实验解析的蛋白-配体复合物结构。借助类似现代搜索引擎的稠密检索(dense retrieval)技术,可以高效地使用蛋白靶点查询超大规模化合物库。
结果表明,在DUD-E和LIT-PCBA 2个广泛使用的虚拟筛选基准数据集上,DrugCLIP在速度与准确性方面均优于传统分子对接方法以及当前最先进的深度学习模型。该方法在不同化学骨架、不同蛋白家族之间同样表现出良好的泛化能力,并且对结构扰动具有较强鲁棒性。在实验验证中,DrugCLIP成功识别出5-羟色胺2A受体(5HT2AR)和去甲肾上腺素转运体(NET)的高效配体,这2个靶点在精神类疾病中具有关键作用。其中,2种5HT2AR激动剂的半数有效浓度(EC50)均低于100 nmol/L;2种NET抑制剂则通过冷冻电镜结构解析获得了结构验证。
为提升对AlphaFold预测结构的适用性,研究团队进一步开发了GenPack——一种生成式口袋精修模块,用以提高结合口袋识别的精度。研究团队将DrugCLIP应用于一次全基因组虚拟筛选任务:针对约1万个人类蛋白,对5亿个化合物进行筛选。在仅使用8块GPU的情况下,模型在24 h内完成了超过10万亿个蛋白-配体对的打分。该筛选共获得200多万个候选分子,覆盖约2万个结合口袋,相当于人类基因组的1/2。所有筛选数据均已公开,以支持广泛的药物研发应用。
研发人员表示,未来DrugCLIP将与科研及产业生态合作伙伴深度协作,聚焦抗癌、传染病、罕见病等领域,加速新靶点与First-in-class药物(首创新药)发现。团队将持续优化引擎性能、拓展支持模态,助力构建更智能、高效、普惠的全球药物创新生态。
(综合:《Science》、清华大学官网、中国科技网、《中国教育报》《光明日报》)

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  • 首发时间:2026-02-11
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2种不同金属材料的力学参数
科
Family
属数
Number of
genus
种数
Number of
species
占总种数比例
Percentage of
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属
Genus
种数
Number of
species
占总种数比例
Percentage of total
species (%)
鹅膏菌科Amanitaceae 2 11 5.26 鹅膏菌属 Amanita 10 4.78
小菇科 Mycenaceae 2 12 5.74 丝盖伞属 Inocybe 5 2.39
多孔菌科 Polyporaceae 8 14 6.70 蜡蘑属 Laccaria 5 2.39
红菇科 Russulaceae 3 23 11.00 小皮伞属 Marasmius 6 2.87
小菇属 Mycena 11 5.26
光柄菇属 Pluteus 5 2.39
红菇属 Russula 17 8.13
栓菌属 Trametes 5 2.39
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