Assessment of protein-ligand complexes in CASP15.

CASP15 introduced a new category, ligand prediction, where participants were provided with a protein or nucleic acid sequence, SMILES line notation, and stoichiometry for ligands and tasked with generating computational models for the three-dimensional structure of the corresponding protein-ligand complex. These models were subsequently compared with experimental structures determined by x-ray crystallography or cryoEM. To assess these predictions, two novel scores were developed. The Binding-Site Superposed, Symmetry-Corrected Pose Root Mean Square Deviation (BiSyRMSD) evaluated the absolute deviations of the models from the experimental structures. At the same time, the Local Distance Difference Test for Protein-Ligand Interactions (lDDT-PLI) assessed the ability of models to reproduce the protein-ligand interactions in the experimental structures. The ligands evaluated in this challenge range from single-atom ions to large flexible organic molecules. More than 1800 submissions were evaluated for their ability to predict 23 different protein-ligand complexes. Overall, the best models could faithfully reproduce the geometries of more than half of the prediction targets. The ligands' size and flexibility were the primary factors influencing the predictions' quality. Small ions and organic molecules with limited flexibility were predicted with high fidelity, while reproducing the binding poses of larger, flexible ligands proved more challenging.

Critical assessment of AI in drug discovery.

Introduction: Artificial Intelligence (AI) has become a component of our everyday lives, with applications ranging from recommendations on what to buy to the analysis of radiology images. Many of the techniques originally developed for other fields such as language translation and computer vision are now being applied in drug discovery. AI has enabled multiple aspects of drug discovery including the analysis of high content screening data, and the design and synthesis of new molecules.Areas covered: This perspective provides an overview of the application of AI in several areas relevant to drug discovery including property prediction, molecule generation, image analysis, and organic synthesis planning.Expert opinion: While a variety of machine learning methods are now being routinely used to predict biological activity and ADME properties, methods of representing molecules continue to evolve. Molecule generation methods are relatively new and unproven but hold the potential to access new, unexplored areas of chemical space. The application of AI in drug discovery will continue to benefit from dedicated research, as well as AI developments in other fields. With this pairing algorithmic advancements and high-quality data, the impact of AI in drug discovery will continue to grow in the coming years.

Going further than Lipinski's rule in drug design.

INTRODUCTION: Lipinski's 1997 publication of the 'Rule of 5' (Ro5) was one of the most influential recent medicinal chemistry publications. In the almost 15 years since the publication of the original Ro5 paper, multiple groups have refined and expanded on the Ro5 and proposed additional heuristics to guide medicinal chemistry programs. While many variations on the Ro5 have been proposed, the majority of these remain close to the original guidelines proposed by Lipinski et al. AREAS COVERED: This review provides an overview of heuristic methods for the design of drug-like molecules. The article also provides the reader with suggestions on future directions for the field. EXPERT OPINION: While Lipinski's publication of the Ro5 and subsequent work by other authors has made medicinal chemists more aware of the relationships between physical properties and ADMET, it has hardly been a panacea. Pharmaceutical productivity continues to lag, and the industry is exploring new models to improve its output. If we are to progress, we need to move beyond simple models based on lipophilicity and gain a deeper understanding of molecular interactions.