Winkler David A
Monash Institute of Pharmaceutical Sciences, Monash University, Parkville, VIC, Australia.
Latrobe Institute for Molecular Science, La Trobe University, Bundoora, VIC, Australia.
Front Chem. 2021 Mar 15;9:614073. doi: 10.3389/fchem.2021.614073. eCollection 2021.
Neglected tropical diseases continue to create high levels of morbidity and mortality in a sizeable fraction of the world's population, despite ongoing research into new treatments. Some of the most important technological developments that have accelerated drug discovery for diseases of affluent countries have not flowed down to neglected tropical disease drug discovery. Pharmaceutical development business models, cost of developing new drug treatments and subsequent costs to patients, and accessibility of technologies to scientists in most of the affected countries are some of the reasons for this low uptake and slow development relative to that for common diseases in developed countries. Computational methods are starting to make significant inroads into discovery of drugs for neglected tropical diseases due to the increasing availability of large databases that can be used to train ML models, increasing accuracy of these methods, lower entry barrier for researchers, and widespread availability of public domain machine learning codes. Here, the application of artificial intelligence, largely the subset called machine learning, to modelling and prediction of biological activities and discovery of new drugs for neglected tropical diseases is summarized. The pathways for the development of machine learning methods in the short to medium term and the use of other artificial intelligence methods for drug discovery is discussed. The current roadblocks to, and likely impacts of, synergistic new technological developments on the use of ML methods for neglected tropical disease drug discovery in the future are also discussed.
尽管针对新疗法的研究仍在进行，但被忽视的热带病在世界相当一部分人口中仍造成了高发病率和高死亡率。一些加速了富裕国家疾病药物研发的最重要技术进展，并未惠及被忽视热带病的药物研发。制药开发商业模式、新药治疗的研发成本及后续患者成本，以及大多数受影响国家的科学家获取技术的难易程度，都是导致相对于发达国家常见疾病而言，被忽视热带病药物研发采用率低且进展缓慢的部分原因。由于可用于训练机器学习模型的大型数据库越来越多、这些方法的准确性不断提高、研究人员的进入门槛降低以及公共领域机器学习代码的广泛可得，计算方法开始在被忽视热带病药物研发中取得重大进展。在此，总结了人工智能（主要是机器学习这一子集）在被忽视热带病生物活性建模与预测以及新药发现中的应用。讨论了机器学习方法在短期到中期的发展路径以及其他人工智能方法在药物研发中的应用。还讨论了当前阻碍协同新技术发展应用于未来被忽视热带病药物研发中机器学习方法的障碍，以及这些发展可能产生的影响。
