In Silico ADME Modeling

Accelerated Discovery of Carbamate Cbl-b Inhibitors Using Generative AI Models and Structure-Based Drug Design

Casitas B-lymphoma proto-oncogene-b (Cbl-b) is a RING finger E3 ligase that has an important role in effector T cell function, acting as a negative regulator of T cell, natural killer (NK) cell, and B cell activation. A discovery effort toward Cbl-b inhibitors was pursued in which a generative AI design engine, REINVENT, was combined with a medicinal chemistry structure-based design to discover novel inhibitors of Cbl-b. Key to the success of this effort was the evolution of the "Design" phase of the Design-Make-Test-Analyze cycle to involve iterative rounds of an in silico structure-based drug design, strongly guided by physics-based affinity prediction and machine learning DMPK predictive models, prior to selection for synthesis. This led to the accelerated discovery of a potent series of carbamate Cbl-b inhibitors.

Design and Evaluation of a Low Hydrogen Bond Donor Count Fragment Screening Set to Aid Hit Generation of PROTACs Intended for Oral Delivery

The development of orally bioavailable PROTACs presents a significant challenge due to the inflated physicochemical properties of such heterobifunctional molecules. Molecules occupying this "beyond rule of five" space often demonstrate limited oral bioavailability due to the compounding effects of elevated molecular weight and hydrogen bond donor count (among other properties), but it is possible to achieve sufficient oral bioavailability through physicochemical optimization. Herein, we disclose the design and evaluation of a low hydrogen bond donor count (≤1 HBD) fragment screening set to aid hit generation of PROTACs intended for an oral route of delivery. We demonstrate that application of this library can enhance fragment screens against PROTAC proteins of interest and ubiquitin ligases, yielding fragment hits containing ≤1 HBD suitable for optimizing toward orally bioavailable PROTACs.

Artificial Intelligence in Drug Metabolism and Excretion Prediction: Recent Advances, Challenges, and Future Perspectives

Drug metabolism and excretion play crucial roles in determining the efficacy and safety of drug candidates, and predicting these processes is an essential part of drug discovery and development. In recent years, artificial intelligence (AI) has emerged as a powerful tool for predicting drug metabolism and excretion, offering the potential to speed up drug development and improve clinical success rates. This review highlights recent advances in AI-based drug metabolism and excretion prediction, including deep learning and machine learning algorithms. We provide a list of public data sources and free prediction tools for the research community. We also discuss the challenges associated with the development of AI models for drug metabolism and excretion prediction and explore future perspectives in the field. We hope this will be a helpful resource for anyone who is researching in silico drug metabolism, excretion, and pharmacokinetic properties.

Recent Studies of Artificial Intelligence on In Silico Drug Distribution Prediction

Drug distribution is an important process in pharmacokinetics because it has the potential to influence both the amount of medicine reaching the active sites and the effectiveness as well as safety of the drug. The main causes of 90% of drug failures in clinical development are lack of efficacy and uncontrolled toxicity. In recent years, several advances and promising developments in drug distribution property prediction have been achieved, especially in silico, which helped to drastically reduce the time and expense of screening undesired drug candidates. In this study, we provide comprehensive knowledge of drug distribution background, influencing factors, and artificial intelligence-based distribution property prediction models from 2019 to the present. Additionally, we gathered and analyzed public databases and datasets commonly utilized by the scientific community for distribution prediction. The distribution property prediction performance of five large ADMET prediction tools is mentioned as a benchmark for future research. On this basis, we also offer future challenges in drug distribution prediction and research directions. We hope that this review will provide researchers with helpful insight into distribution prediction, thus facilitating the development of innovative approaches for drug discovery.

Designing Selective Drug-like Molecular Glues for the Glucocorticoid Receptor/14-3-3 Protein-Protein Interaction

The ubiquitously expressed glucocorticoid receptor (GR) is a nuclear receptor that controls a broad range of biological processes and is activated by steroidal glucocorticoids such as hydrocortisone or dexamethasone. Glucocorticoids are used to treat a wide variety of conditions, from inflammation to cancer but suffer from a range of side effects that motivate the search for safer GR modulators. GR is also regulated outside the steroid-binding site through protein-protein interactions (PPIs) with 14-3-3 adapter proteins. Manipulation of these PPIs will provide insights into noncanonical GR signaling as well as a new level of control over GR activity. We report the first molecular glues that selectively stabilize the 14-3-3/GR PPI using the related nuclear receptor estrogen receptor α (ERα) as a selectivity target to drive design. These 14-3-3/GR PPI stabilizers can be used to dissect noncanonical GR signaling and enable the development of novel atypical GR modulators.

Discovery by Virtual Screening of an Inhibitor of CDK5-Mediated PPARγ Phosphorylation

Thiazolidinedione PPARγ agonists such as rosiglitazone and pioglitazone are effective antidiabetic drugs, but side effects have limited their use. It has been posited that their positive antidiabetic effects are mainly mediated by the inhibition of the CDK5-mediated Ser273 phosphorylation of PPARγ, whereas the side effects are linked to classical PPARγ agonism. Thus compounds that inhibit PPARγ Ser273 phosphorylation but lack classical PPARγ agonism have been sought as safer antidiabetic therapies. Herein we report the discovery by virtual screening of 10, which is a potent PPARγ binder and in vitro inhibitor of the CDK5-mediated phosphorylation of PPARγ Ser273 and displays negligible PPARγ agonism in a reporter gene assay. The pharmacokinetic properties of 10 are compatible with oral dosing, enabling preclinical in vivo testing, and a 7 day treatment demonstrated an improvement in insulin sensitivity in the ob/ob diabetic mouse model.