miDruglikeness: Subdivisional Drug-Likeness Prediction Models Using Active Ensemble Learning Strategies.

The drug development pipeline involves several stages including in vitro assays, in vivo assays, and clinical trials. For candidate selection, it is important to consider that a compound will successfully pass through these stages. Using graph neural networks, we developed three subdivisional models to individually predict the capacity of a compound to enter in vivo testing, clinical trials, and market approval stages. Furthermore, we proposed a strategy combing both active learning and ensemble learning to improve the quality of the models. The models achieved satisfactory performance in the internal test datasets and four self-collected external test datasets. We also employed the models as a general index to make an evaluation on a widely known benchmark dataset DEKOIS 2.0, and surprisingly found a powerful ability on virtual screening tasks. Our model system (termed as miDruglikeness) provides a comprehensive drug-likeness prediction tool for drug discovery and development.

Transfer Learning for Drug Discovery.

The data sets available to train models for in silico drug discovery efforts are often small. Indeed, the sparse availability of labeled data is a major barrier to artificial-intelligence-assisted drug discovery. One solution to this problem is to develop algorithms that can cope with relatively heterogeneous and scarce data. Transfer learning is a type of machine learning that can leverage existing, generalizable knowledge from other related tasks to enable learning of a separate task with a small set of data. Deep transfer learning is the most commonly used type of transfer learning in the field of drug discovery. This Perspective provides an overview of transfer learning and related applications to drug discovery to date. Furthermore, it provides outlooks on the future development of transfer learning for drug discovery.

Deep learning for molecular generation.

De novo drug design aims to generate novel chemical compounds with desirable chemical and pharmacological properties from scratch using computer-based methods. Recently, deep generative neural networks have become a very active research frontier in de novo drug discovery, both in theoretical and in experimental evidence, shedding light on a promising new direction of automatic molecular generation and optimization. In this review, we discussed recent development of deep learning models for molecular generation and summarized them as four different generative architectures with four different optimization strategies. We also discussed future directions of deep generative models for de novo drug design.

Efficient molecular encoders for virtual screening.

Molecular representations encoding molecular structure information play critical roles in molecular virtual screening (VS). In order to improve VS performance, an abundance of molecular encoders have been developed and tested by various VS challenges. Combinational strategies were also used to improve the performance. Deep learning (DL)-based molecular encoders have attracted much attention for their automatic information extraction ability. In this review, we present an overview of two-dimensional-, three-dimensional-, and DL-based molecular encoders, summarize recent progress of VS using DL technologies, and propose a general framework of DL molecular encoder-based VS. Perspectives on the future directions of molecular representations and applications in the prediction of active compounds are also provided.