Drug development is expensive, time-consuming, and has a high failure rate. In recent
years, artificial intelligence (AI) has emerged as a transformative tool in drug discovery …

The factors determining a drug's success are manifold, making de novo drug design an
inherently multi-objective optimisation (MOO) problem. With the advent of machine learning …

Abstract ChEMBL (https://www. ebi. ac. uk/chembl/) is a manually curated, high-quality, large-
scale, open, FAIR and Global Core Biodata Resource of bioactive molecules with drug-like …

The discovery of new drugs has important implications for human health. Traditional
methods for drug discovery rely on experiments to optimize the structure of lead molecules …

Anti-cancer drug design has been acknowledged as a complicated, expensive, time-
consuming, and challenging task. How to reduce the research costs and speed up the …

Lead optimization is a crucial step in the drug discovery process, which aims to design
potential drug candidates from biologically active hits. During lead optimization, active hits …

The advent of artificial intelligence (AI) has catalyzed a profound transformation in the
pharmaceutical industry, ushering in a paradigm shift across various domains, including …

Abstract Drug Research and Development (R&D) is a complex and difficult process, and
current drug R&D faces the challenges of long time span, high investment, and high failure …

As a subfield of deep learning (DL), generative adversarial networks (GANs) have produced
impressive generative results by applying deep generative models to create synthetic data …

The emergence of neuromorphic computing, inspired by the structure and function of the
human brain, presents a transformative framework for modelling neurological disorders in …