Machine Learning to Predict Teratogenicity: Theory and Practice.

Abstract

Machine learning (ML) is a subfield of artificial intelligence (AI) that consists of developing algorithms that can automatically learn patterns and relationships from data, without being explicitly programmed. It continues to advance with the development of more sophisticated algorithms, increased computational power, and larger datasets, leading to significant advancements in AI technology. With the significant progress made in ML, the need to apply these systems in the area of teratogenicity is growing. It is sought as robust boosting methods to overcome many limitations and restrictions facing the experimental studies. By performing tasks such as classification, regression, clustering, anomaly detection, and decision systems, ML can be used to assess whether an agent is teratogen or not or to determine its teratogenic potential. It may also be used for the purpose of deciding on the use of medicinal products. In this chapter, we describe how ML can be used to investigate teratogenicity.