TOWARDS AUTOMATED MOLECULAR SEARCH IN DRUG SPACE

Abstract

Recent advances in convolutional neural networks have inspired the application of deep learning to other disciplines. Even though image processing and natural language processing have turned out to be the most successful, there are many other areas that have benefited, like computational chemistry in general and drug design in particular. From 2018 the scientific community has seen a surge of methodologies related to the generation of diverse molecular libraries using machine learning. The first goal is to provide an accessible way of using machine learning algorithms to chemists without technical knowledge. Hence, cheML.io, a web database that contains virtual molecules generated by 10 recent ML algorithms, is proposed. It allows users to browse the data in a user-friendly and convenient manner. ML-generated molecules with desired structures and properties can be retrieved with the help of a drawing widget. For the case of a specific search leading to insufficient results, users are able to create new molecules on demand. The second goal is to develop an algorithm that allows the generation of diverse focused libraries utilizing one, or two seed molecules which guide the generation of de novo molecules. Here a variant of transformers, an architecture recently developed for natural language processing, was employed for this purpose. The results indicate that this model is indeed applicable for the task of generating focussed molecular libraries and leads to statistically significant increases in some of the core metrics of the MOSES benchmark. A benchmark that provides baselines and metrics that can characterize the main attributes of the algorithms by examining the generated molecules. In addition, a novel way of generating libraries where two seed molecules can be fused is introduced.