在 silico 中,ADME/tox 成熟:二十年后
Sean Ekins1, Thomas R Lane1, Fabio Urbina1
1Collaborations Pharmaceuticals, Inc., Raleigh, North Carolina, USA.
概括
计算药物发现已经显著发展,从单一属性预测转向优化多个吸收,分布,新陈代谢,分泌和毒性 (ADME/Tox) 属性,同时使用先进的in silico工具.
科学领域:
- 药物发现 药物发现 药物发现
- 计算化学的计算化学
- 药理动力学 药理动力学
背景情况:
- 2000年代初,用于预测吸收,分布,新陈代谢,分泌和毒性 (ADME/Tox) 的计算方法的兴起,以缓解晚期药物开发失败.
- 在过去的二十年里,在in silico ADME/Tox预测能力上进行了大量投资,这是科学出版物所证明的.
研究的目的:
- 为了反思 in silico ADME/Tox预测从早期阶段到当前能力的演变.
- 将过去的预测方法和基础设施需求与药物发现的现状进行比较.
- 在计算ADME/Tox建模中识别当前的挑战和未来的机会.
主要方法:
- 审查历史出版物和行业趋势在in silico药物发现.
- 在药物设计中,分析从单属性优化转向多属性优化的转变.
- 检查生成性de novo设计和大型语言模型对ADME/Tox预测的影响.
主要成果:
- 药物发现实践已经从优化单个ADME/Tox属性过渡到同时优化多个属性.
- 该领域已经从需要广泛的基础设施发展到利用生成的de novo设计和大型语言模型.
- 目前的ADME/Tox模型面临着非常大的分子的挑战,这表明了未来发展的领域.
结论:
- 在 silico ADME / Tox 预测领域已经成熟,使多种药物特性同时优化成为可能.
- 像生成设计和LLM这样的新兴技术正在改变药物发现工作流.
- 未来的进展将从增加公共ADME/Tox数据和扩展的算法方法中受益.
相关概念视频
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