Intestinal mucosal barrier repair and immune regulation with an AI-developed gut-restricted PHD inhibitor.

Hypoxia-inducible factor prolyl hydroxylase (PHD) inhibitors have been approved for treating renal anemia yet have failed clinical testing for inflammatory bowel disease because of a lack of efficacy. Here we used a multimodel multimodal generative artificial intelligence platform to design an orally gut-restricted selective PHD1 and PHD2 inhibitor that exhibits favorable safety and pharmacokinetic profiles in preclinical studies. ISM012-042 restores intestinal barrier function and alleviates gut inflammation in multiple experimental colitis models.

PandaOmics: An AI-Driven Platform for Therapeutic Target and Biomarker Discovery.

PandaOmics is a cloud-based software platform that applies artificial intelligence and bioinformatics techniques to multimodal omics and biomedical text data for therapeutic target and biomarker discovery. PandaOmics generates novel and repurposed therapeutic target and biomarker hypotheses with the desired properties and is available through licensing or collaboration. Targets and biomarkers generated by the platform were previously validated in both and studies.

Prediction of Clinical Trials Outcomes Based on Target Choice and Clinical Trial Design with Multi-Modal Artificial Intelligence.

Drug discovery and development is a notoriously risky process with high failure rates at every stage, including disease modeling, target discovery, hit discovery, lead optimization, preclinical development, human safety, and efficacy studies. Accurate prediction of clinical trial outcomes may help significantly improve the efficiency of this process by prioritizing therapeutic programs that are more likely to succeed in clinical trials and ultimately benefit patients. Here, we describe inClinico, a transformer-based artificial intelligence software platform designed to predict the outcome of phase II clinical trials.

The Hitchhiker's Guide to Deep Learning Driven Generative Chemistry.

This microperspective covers the most recent research outcomes of artificial intelligence (AI) generated molecular structures from the point of view of the medicinal chemist. The main focus is on studies that include synthesis and experimental validation in biochemical assays of the generated molecular structures, where we analyze the reported structures' relevance in modern medicinal chemistry and their novelty. The authors believe that this review would be appreciated by medicinal chemistry and AI-driven drug design (AIDD) communities and can be adopted as a comprehensive approach for qualifying different research outcomes in AIDD.

Quantum computing for near-term applications in generative chemistry and drug discovery.

In recent years, drug discovery and life sciences have been revolutionized with machine learning and artificial intelligence (AI) methods. Quantum computing is touted to be the next most significant leap in technology; one of the main early practical applications for quantum computing solutions is predicted to be in quantum chemistry simulations. Here, we review the near-term applications of quantum computing and their advantages for generative chemistry and highlight the challenges that can be addressed with noisy intermediate-scale quantum (NISQ) devices.

Chemistry42: An AI-Driven Platform for Molecular Design and Optimization.

Chemistry42 is a software platform for small molecule design and optimization that integrates Artificial Intelligence (AI) techniques with computational and medicinal chemistry methodologies. Chemistry42 efficiently generates novel molecular structures with optimized properties validated in both and studies and is available through licensing or collaboration. Chemistry42 is the core component of Insilico Medicine's drug discovery suite.

Revisiting the effects of sequence and structure on the hydrogen bonding and π-stacking interactions in nucleic acids.

Calculated electron densities from PBE0/6-31+G(d,p) were analyzed with respect to the hydrogen bonding within a nucleic acid base pair and the π-stacking between sets of base pairs. From published X-ray crystallographic data, base pairs were isolated from a total of 11 DNA and RNA duplexes, and their experimental geometry was maintained throughout the analyses. Focusing solely on Watson-Crick base pairs, from the values of the electron density between interacting nuclei (at the bond critical points), we provide quantitative data on individual weak interactions.

New insights into the use of (TD-)DFT for geometries and electronic structures of constrained pi-stacked systems: [n.n]paracyclophanes.

We have evaluated the performance of three (TD-)DFT functionals with the 6-31+G(d,p) basis set for the reproduction of experimental geometries, vertical ionization potentials, and low excitation energies of a selection of [2.2] and [3.3]paracyclophanes.