pBRICS: A Novel Fragmentation Method for Explainable Property Prediction of Drug-Like Small Molecules

Target-specific novel molecules with their recipe: Incorporating synthesizability in the design process

Application of Artificial intelligence (AI) in drug discovery has led to several success stories in recent times. While traditional methods mostly relied upon screening large chemical libraries for early-stage drug-design, de novo design can help identify novel target-specific molecules by sampling from a much larger chemical space. Although this has increased the possibility of finding diverse and novel molecules from previously unexplored chemical space, this has also posed a great challenge for medicinal chemists to synthesize at least some of the de novo designed novel molecules for experimental validation. To address this challenge, in this work, we propose a novel forward synthesis-based generative AI method, which is used to explore the synthesizable chemical space. The method uses a structure-based drug design framework, where the target protein structure and a target-specific seed fragment from co-crystal structures can be the initial inputs. A random fragment from a purchasable fragment library can also be the input if a target-specific fragment is unavailable. Then a template-based forward synthesis route prediction and molecule generation is performed in parallel using the Monte Carlo Tree Search (MCTS) method where, the subsequent fragments for molecule growth can again be obtained from a purchasable fragment library. The rewards for each iteration of MCTS are computed using a drug-target affinity (DTA) model based on the docking pose of the generated reaction intermediates at the binding site of the target protein of interest. With the help of the proposed method, it is now possible to overcome one of the major obstacles posed to the AI-based drug design approaches through the ability of the method to design novel target-specific synthesizable molecules.

Transfer learning empowers accurate pharmacokinetics prediction of small samples

Accurate assessment of pharmacokinetic (PK) properties is crucial for selecting optimal candidates and avoiding downstream failures. Transfer learning is an innovative machine learning approach enabling high-throughput prediction with limited data. Recently, transfer learning methods showed promise in predicting ADME/PK parameters. Given the prolific growth of research on transfer learning for PK prediction, a comprehensive review of its advantages and challenges is imperative. This study explores the fundamentals, classifications, toolkits and applications of various transfer learning techniques for PK prediction, demonstrating their utility through three practical case studies. This work will serve as a reference for drug design researchers.

Peptide-drug conjugates: A new paradigm for targeted cancer therapy

Peptide-drug conjugates (PDCs) are the new hope for targeted therapy after antibody-drug conjugates (ADCs). Compared with ADCs, the core advantages of PDCs are enhanced tissue penetration, easier chemical synthesis, and lower production costs. Two PDCs have been approved by the US Food and Drug Administration (FDA) for the treatment of cancer. The therapeutic effects of PDCs are remarkable, but PDCs also encounter problems when used as targeted therapeutics, such as poor stability, a short blood circulation time, a long research and development time frame, and a slow clinical development process. Therefore, it is very urgent and important to understand the latest research progress of cancer cells targeting PDC, the solution to its stability problem, the scheme of computer technology to assist its research and development, and the direction of its future development. In this manuscript, based on the structure and function of PDCs, the latest research progress on PDCs from the aspects of cancer cell-targeting peptide (CTP) selection, pharmacokinetic characteristics, stability regulation and so on were systematically reviewed, hoping to highlight the current problems and future development directions of PDCs.