Combining generative artificial intelligence and on-chip synthesis for de novo drug design.

Automating the molecular design-make-test-analyze cycle accelerates hit and lead finding for drug discovery. Using deep learning for molecular design and a microfluidics platform for on-chip chemical synthesis, liver X receptor (LXR) agonists were generated from scratch. The computational pipeline was tuned to explore the chemical space of known LXRα agonists and generate novel molecular candidates.

DNA triplex structure, thermodynamics, and destabilisation: insight from molecular simulations.

Molecular dynamics simulations are used to elucidate the structure and thermodynamics of DNA triplexes associated with the neurodegenerative disease Friedreich's ataxia (FRDA), as well as complexes of these triplexes with the small molecule netropsin, which is known to destabilise triplexes. The ability of molecular simulations in explicit solvent to accurately capture triplex thermodynamics is verified for the first time, with the free energy to dissociate a 15-base antiparallel purine triplex-forming oligomer (TFO) from the duplex found to be slightly higher than reported experimentally. The presence of netropsin in the minor groove destabilises the triplex as expected, reducing the dissociation free energy by approximately 50%.

Hybrid Network Model for "Deep Learning" of Chemical Data: Application to Antimicrobial Peptides.

We present a "deep" network architecture for chemical data analysis and classification together with a prospective proof-of-concept application. The model features a self-organizing map (SOM) as the input layer of a feedforward neural network. The SOM converts molecular descriptors to a two-dimensional image for further processing.

Scoring of de novo Designed Chemical Entities by Macromolecular Target Prediction.

Computational de novo molecular design and macromolecular target prediction have become routine in applied cheminformatics. In this study, we have generated populations of drug template-derived designs using ligand-based building block assembly, and predicted their potential targets. The results of our analysis show that the reaction-based de novo design generated new chemical entities with similar properties and pharmacophores as that of the template drugs as well as up to 44 % of the de novo compounds receiving the correct target predictions.