MolOptimizer: A Molecular Optimization Toolkit for Fragment-Based Drug Design.

MolOptimizer is a user-friendly computational toolkit designed to streamline the hit-to-lead optimization process in drug discovery. MolOptimizer extracts features and trains machine learning models using a user-provided, labeled, and small-molecule dataset to accurately predict the binding values of new small molecules that share similar scaffolds with the target in focus. Hosted on the Azure web-based server, MolOptimizer emerges as a vital resource, accelerating the discovery and development of novel drug candidates with improved binding properties.

Inferring primase-DNA specific recognition using a data driven approach.

DNA-protein interactions play essential roles in all living cells. Understanding of how features embedded in the DNA sequence affect specific interactions with proteins is both challenging and important, since it may contribute to finding the means to regulate metabolic pathways involving DNA-protein interactions. Using a massive experimental benchmark dataset of binding scores for DNA sequences and a machine learning workflow, we describe the binding to DNA of T7 primase, as a model system for specific DNA-protein interactions. Effective binding of T7 primase to its specific DNA recognition sequences triggers the formation of RNA primers that serve as Okazaki fragment start sites during DNA replication.

Discovery of small-molecule inhibitors targeting the ribosomal peptidyl transferase center (PTC) of M. tuberculosis.

M. tuberculosis (Mtb) is a pathogenic bacterium that causes tuberculosis, which kills more than 1.5 million people worldwide every year. Strains resistant to available antibiotics pose a significant healthcare problem. The enormous complexity of the ribosome poses a barrier for drug discovery. We have overcome this in a tractable way by using an RNA segment that represents the peptidyl transferase center as a target. By using a novel combination of NMR transverse relaxation times (T 2) and computational chemistry approaches, we have obtained improved inhibitors of the Mtb ribosomal PTC. Two phenylthiazole derivatives were predicted by machine learning models as effective inhibitors, and this was confirmed by their IC50 values, which were significantly improved over standard antibiotic drugs.