The effect of cancer-associated mutations on ligand binding and receptor function - A case for the 5-HT receptor.

The serotonin 5-HT receptor is a G protein-coupled receptor (GPCR) mainly expressed in the central nervous system. Besides regulating mood, appetite, and reproductive behavior, it has been identified as a potential target for cancer treatment. In this study, we aimed to investigate the effects of cancer patient-derived 5-HT receptor mutations on ligand binding and receptor functionality.

Artificial intelligence in multi-objective drug design.

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 and optimisation methods, the field of multi-objective compound design has seen a rapid increase in developments and applications. Population-based metaheuris-tics and deep reinforcement learning are the most commonly used artificial intelligence methods in the field, but recently conditional learning methods are gaining popularity.

Oncological drug discovery: AI meets structure-based computational research.

The integration of machine learning and structure-based methods has proven valuable in the past as a way to prioritize targets and compounds in early drug discovery. In oncological research, these methods can be highly beneficial in addressing the diversity of neoplastic diseases portrayed by the different hallmarks of cancer. Here, we review six use case scenarios for integrated computational methods, namely driver prediction, computational mutagenesis, (off)-target prediction, binding site prediction, virtual screening, and allosteric modulation analysis.

Characterization of cancer-related somatic mutations in the adenosine A receptor.

In cancer, G protein-coupled receptors (GPCRs) are involved in tumor progression and metastasis. In this study we particularly examined one GPCR, the adenosine A receptor. This receptor is activated by high concentrations of its endogenous ligand adenosine, which suppresses the immune response to fight tumor progression.

Proteochemometrics - recent developments in bioactivity and selectivity modeling.

Proteochemometrics is a machine learning based modeling approach relying on a combination of ligand and protein descriptors. With ongoing developments in machine learning and increases in public data the technique is more frequently applied in early drug discovery, typically in ligand-target binding prediction. Common applications include improvements to single target quantitative structure-activity relationship models, protein selectivity and promiscuity modeling, and large-scale deep learning approaches.