通过积极的深度学习穿越化学空间,用于低数据的药物发现
Derek van Tilborg1,2, Francesca Grisoni3,4
1Institute for Complex Molecular Systems (ICMS), Department of Biomedical Engineering, Eindhoven University of Technology, Eindhoven, The Netherlands.
积极的深度学习显著提高了药物发现,特别是在有限的数据. 与传统方法相比,这种方法提高了命中发现的6倍,克服了数据大小和多样性的挑战.
科学领域:
- 计算化学是一种计算化学.
- 人工智能在药物发现中的作用
- 机器学习用于分子选.
背景情况:
- 药物发现面临挑战,因为当前深度学习方法的数据大小有限和分子多样性有限.
- 积极的深度学习提供了低数据药物发现的潜力,通过在查过程中实现代模型改进.
- 由于缺乏对最佳计算策略,对传统方法的比较性能和特定的低数据应用的了解,积极深度学习的更广泛采用受到阻碍.
研究的目的:
- 解决应用主动深度学习到药物发现的关键局限性.
- 在模拟的低数据场景中系统地分析主动学习策略和深度学习架构.
- 在低数据的药物发现制度中确定成功的关键因素.
主要方法:
- 模拟了一个低数据的药物发现环境.
- 系统地分析了六种积极学习策略.
- 在三个大型分子库中评估了两个深度学习架构.
主要成果:
- 确定了在低数据环境中成功发现药物的关键决定因素.
- 证明积极学习策略可以显著优于传统选方法.
- 在使用主动学习的过程中,成功发现率提高了六倍.
结论:
- 积极的深度学习是加速药物发现的强大工具,特别是当数据稀缺时.
- 该研究为优化低数据药物发现的积极学习策略提供了关键的见解.
- 积极学习在有效地识别潜在的药物候选者方面,与传统查相比,具有显著的优势.
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