cMolGPT:一种条件生成的预训练变压器,用于针对特定目标的De Novo分子生成
Ye Wang1, Honggang Zhao2, Simone Sciabola1
1Biotherapeutic and Medicinal Sciences, Biogen, 225 Binney Street, Cambridge, MA 02142, USA.
Molecules (Basel, Switzerland)
|June 10, 2023
概括
我们开发了一种新的产生预训练变压器 (GPT) 灵感模型,用于向药物设计. 这种条件模型 (cMolGPT) 产生了针对向蛋白质的新型药物样分子,加速了药物发现.
科学领域:
- 药用化学 医学化学
- 计算化学计算化学
- 人工智能在药物发现中的作用
背景情况:
- 深度生成模型越来越多地用于小分子药物设计中的新型化合物生成.
- 设计专门与蛋白相互作用的分子仍然是一个关键的挑战.
研究的目的:
- 提出一种以生成预训练变压器 (GPT) 为灵感的模型,用于新的特定目标分子设计.
- 为了产生与药物类似的化合物,这些化合物对特定的蛋白质点具有活性.
主要方法:
- 实施条件生成预训练变压器 (cMolGPT) 模型.
- 利用针对特定目标的关键和值的多头注意力.
- 为分子表示生成SMILES字符串.
主要成果:
- cMolGPT模型成功地产生了类似药物和活性化合物.
- 生成的化合物表现出与真实目标特定分子的化学空间相似性.
- 该模型产生了大量的新型化合物.
结论:
- 条件生成预训练变压器 (cMolGPT) 是新分子设计的一个有价值的工具.
- 这种方法有可能加速药物发现中的分子优化周期时间.
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