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Pharmacophore

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DockNet: high-throughput protein-protein interface contact prediction.

Bioinformatics (Oxford, England)
MOTIVATION: Over 300 000 protein-protein interaction (PPI) pairs have been identified in the human proteome and targeting these is fast becoming the next frontier in drug design. Predicting PPI sites, however, is a challenging task that traditionally...

Discovery of novel PARP-1 inhibitors using tandem studies: integrated docking, e-pharmacophore, deep learning based de novo and molecular dynamics simulation approach.

Journal of biomolecular structure & dynamics
Cancer accounts for the majority of deaths worldwide, and the increasing incidence of breast cancer is a matter of grave concern. Poly (ADP-ribose) polymerase-1 (PARP-1) has emerged as an attractive target for the treatment of breast cancer as it has...

Identification of inhibitors for Agr quorum sensing system of Staphylococcus aureus by machine learning, pharmacophore modeling, and molecular dynamics approaches.

Journal of molecular modeling
CONTEXT: Staphylococcus aureus is a highly pathogenic organism that is the most common cause of postoperative complications as well as severe infections like bacteremia and infective endocarditis. By mediating the formation of biofilms and the expres...

Probing the molecular mechanisms of α-synuclein inhibitors unveils promising natural candidates through machine-learning QSAR, pharmacophore modeling, and molecular dynamics simulations.

Molecular diversity
Parkinson's disease is characterized by a multifactorial nature that is linked to different pathways. Among them, the abnormal deposition and accumulation of α-synuclein fibrils is considered a neuropathological hallmark of Parkinson's disease. Sever...

Deep learning driven de novo drug design based on gastric proton pump structures.

Communications biology
Existing drugs often suffer in their effectiveness due to detrimental side effects, low binding affinity or pharmacokinetic problems. This may be overcome by the development of distinct compounds. Here, we exploit the rich structural basis of drug-bo...

A pharmacophore-guided deep learning approach for bioactive molecular generation.

Nature communications
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...