Álvarez-Machancoses Óscar, DeAndrés Galiana Enrique J, Cernea Ana, Fernández de la Viña J, Fernández-Martínez Juan Luis
Group of Inverse Problems, Optimization and Machine Learning, Department of Mathematics, University of Oviedo, Oviedo 33007, Spain.
DeepBiosInsights, NETGEV (Maof Tech), Dimona 8610902, Israel.
Pharmgenomics Pers Med. 2020 Mar 19;13:105-119. doi: 10.2147/PGPM.S205082. eCollection 2020.
The complexity of orphan diseases, which are those that do not have an effective treatment, together with the high dimensionality of the genetic data used for their analysis and the high degree of uncertainty in the understanding of the mechanisms and genetic pathways which are involved in their development, motivate the use of advanced techniques of artificial intelligence and in-depth knowledge of molecular biology, which is crucial in order to find plausible solutions in drug design, including drug repositioning. Particularly, we show that the use of robust deep sampling methodologies of the altered genetics serves to obtain meaningful results and dramatically decreases the cost of research and development in drug design, influencing very positively the use of precision medicine and the outcomes in patients. The target-centric approach and the use of strong prior hypotheses that are not matched against reality (disease genetic data) are undoubtedly the cause of the high number of drug design failures and attrition rates. Sampling and prediction under uncertain conditions cannot be avoided in the development of precision medicine.
罕见病十分复杂，尚无有效治疗方法，加之用于分析的基因数据维度高，以及对其发病机制和涉及的基因通路的理解存在高度不确定性，这促使人们使用先进的人工智能技术和深入的分子生物学知识，这对于在药物设计（包括药物重新定位）中找到合理解决方案至关重要。特别是，我们表明，使用强大的深度采样方法来处理改变的基因，有助于获得有意义的结果，并显著降低药物设计中的研发成本，对精准医学的应用和患者的治疗结果产生非常积极的影响。以靶点为中心的方法以及使用与现实（疾病基因数据）不匹配的强先验假设，无疑是药物设计失败率和损耗率居高不下的原因。在精准医学的发展中，无法避免在不确定条件下进行采样和预测。
