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 in vitro and in vivo studies. PandaOmics is a core component of Insilico Medicine's Pharma.ai drug discovery suite, which also includes Chemistry42 for the de novo generation of novel small molecules, and inClinico─a data-driven multimodal platform that forecasts a clinical trial's probability of successful transition from phase 2 to phase 3. In this paper, we demonstrate how the PandaOmics platform can efficiently identify novel molecular targets and biomarkers for various diseases.

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 platform combines an ensemble of clinical trial outcome prediction engines that leverage generative artificial intelligence and multimodal data, including omics, text, clinical trial design, and small molecule properties. inClinico was validated in retrospective, quasi-prospective, and prospective validation studies internally and with pharmaceutical companies and financial institutions. The platform achieved 0.88 receiver operating characteristic area under the curve in predicting the phase II to phase III transition on a quasi-prospective validation dataset. The first prospective predictions were made and placed on date-stamped preprint servers in 2016. To validate our model in a real-world setting, we published forecasted outcomes for several phase II clinical trials achieving 79% accuracy for the trials that have read out. We also present an investment application of inClinico using date stamped virtual trading portfolio demonstrating 35% 9-month return on investment.

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 in vitro 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. We also discuss the possible integration of generative systems running on quantum computers into established generative AI platforms.

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

Chemistry42 is a software platform for de novo 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 in vitro and in vivo studies and is available through licensing or collaboration. Chemistry42 is the core component of Insilico Medicine's Pharma.ai drug discovery suite. Pharma.ai also includes PandaOmics for target discovery and multiomics data analysis, and inClinico─a data-driven multimodal forecast of a clinical trial's probability of success (PoS). In this paper, we demonstrate how the platform can be used to efficiently find novel molecular structures against DDR1 and CDK20.

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. For hydrogen bonding, in addition to quantifying the scissoring effect in GC base pairs, the origin of the controversy around the relative stability of AT and AU base pairs is identified and resolved. Thus, it is illustrated how the conclusion as to their relative stability rests on the specific choice of oligonucleotides compared. For π-stacking, sequence effects for tandem AT base pairs are captured, quantified, and explained, and the greater sensitivity of GC, over AT, sequences to the rise parameter is established. The results presented here show that, from experimental geometries and their electron densities, previously determined effects of the sequence and structure of a duplex on the stabilizing interactions can be captured, quantified, and traced back to the geometry of the base pairs.

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. Overall, (TD-)BH&H outperforms both (TD-)B3LYP and (TD-)PBE0. Some shortcomings are shown by B3LYP for geometries and by BH&H for ionization potentials. Most notably, whereas TD-B3LYP and TD-PBE0 reproduce the wavelength for the first electronic excitation of [n.n]paracyclophanes with weakly interacting aromatic rings, neither handles the strong donor-acceptor interactions in certain substituted [n.n]paracyclophanes, and both seriously underestimate the energy for their first electronic excitation. As the former systems are in many ways similar to stacked nucleic acid bases, we recommend the use of (TD-)PBE0/6-31+G(d,p) for further studies on pi-stacking interactions in constrained systems, such as the base pairs in oligonucleotides.