Pharmacophore-based virtual screening, molecular docking, molecular dynamics simulation, and biological evaluation for the discovery of novel BRD4 inhibitors

A pharmacophore-guided deep learning approach for bioactive molecular generation

The rational design of novel molecules with the desired bioactivity is a critical but challenging task in drug discovery, especially when treating a novel target family or understudied targets. We propose a Pharmacophore-Guided deep learning approach for bioactive Molecule Generation (PGMG). Through the guidance of pharmacophore, PGMG provides a flexible strategy for generating bioactive molecules. PGMG uses a graph neural network to encode spatially distributed chemical features and a transformer decoder to generate molecules. A latent variable is introduced to solve the many-to-many mapping between pharmacophores and molecules to improve the diversity of the generated molecules. Compared to existing methods, PGMG generates molecules with strong docking affinities and high scores of validity, uniqueness, and novelty. In the case studies, we use PGMG in a ligand-based and structure-based drug de novo design. Overall, the flexibility and effectiveness make PGMG a useful tool to accelerate the drug discovery process.

Investigation of novel ligand targeting bromodomain-containing protein 4 (BRD4) for cancer drug discovery: complete pharmacophore approach

The Bromodomain (BRD4) and extra-terminal (BET) protein family are reversible; lysine-acetylated epigenetic readers identified as key important epigenetic regulators for protein recognition in posttranslational modifications for targeting cancer for its role in super-enhancers and transcription of oncogene expression in cancer and other forms of cancer and various diseases. Firstly, JQ-1a small potent BET inhibitors, targeting BET proteins were currently in clinical trials to ablate cancer. The identified compounds were taken from the library of preexisting therapeutically potent molecules. The objective of the present study is to identify the potential small molecule inhibitors against BRD4 through in-silico approach for the treatment of cancer. In present study, designed an in-silico screening of small molecules through ligand-based pharmacophore studies against bromodomain-containing protein 4 (BRD-4) protein and used for virtual screening through Database and their binding affinity and interaction of identified molecules were predicted through molecular docking, molecular dynamics simulations for 12 fixed time period, Molecular mechanics (MMGBSA) binding free energy calculations, ADME with drug-likeness properties including violations of lipinski's rule of 5, Jorgensens rule of 3 and other parameters were studied. The docking results indicate from the reported database screened molecules were validated with docking score -7.92 to -4.27Kcal/mol for BRD4-BD1 and the best model identified 21 hits. Among these two drugs were filtered and scrutinized for their ability based on binding modes and common interaction, MMGBSA of the highest affinity -54.53 Kcal/mol of BRD4-BD1 and ADME properties of selected molecules were predicted for its various parameters, dynamics studies evaluating its binding stability using Maestro software. In Conclusion, two BRD4 inhibitors were found to bind strongly in the similar binding sites as JQ-1, highlighting the role of BRD4-BD1. These compounds were identified as promising new options for regulating epigenetics and understanding the structural needs of BRD4 protein, further research in these areas could lead to the development of more effective and targeted cancer drugs.Communicated by Ramaswamy H. Sarma.