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Drug Design

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In silico design of dehydrophenylalanine containing peptide activators of glucokinase using pharmacophore modelling, molecular dynamics and machine learning: implications in type 2 diabetes.

Journal of computer-aided molecular design
Diabetes represents a significant global health challenge associated with substantial healthcare costs and therapeutic complexities. Current diabetes therapies often entail adverse effects, necessitating the exploration of novel agents. Glucokinase (...

PharmRL: pharmacophore elucidation with deep geometric reinforcement learning.

BMC biology
BACKGROUND: Molecular interactions between proteins and their ligands are important for drug design. A pharmacophore consists of favorable molecular interactions in a protein binding site and can be utilized for virtual screening. Pharmacophores are ...

Artificial intelligence in peptide-based drug design.

Drug discovery today
Protein-protein interactions (PPIs) are fundamental to a variety of biological processes, but targeting them with small molecules is challenging because of their large and complex interaction interfaces. However, peptides have emerged as highly promi...

Targeting protein-ligand neosurfaces with a generalizable deep learning tool.

Nature
Molecular recognition events between proteins drive biological processes in living systems. However, higher levels of mechanistic regulation have emerged, in which protein-protein interactions are conditioned to small molecules. Despite recent advanc...

Targeting Bacterial RNA Polymerase: Harnessing Simulations and Machine Learning to Design Inhibitors for Drug-Resistant Pathogens.

Biochemistry
The increase in antimicrobial resistance presents a major challenge in treating bacterial infections, underscoring the need for innovative drug discovery approaches and novel inhibitors. Bacterial RNA polymerase (RNAP) has emerged as a crucial target...

Advances of deep Neural Networks (DNNs) in the development of peptide drugs.

Future medicinal chemistry
Peptides are able to bind to difficult disease targets with high potency and specificity, providing great opportunities to meet unmet medical requirements. Nevertheless, the unique features of peptides, such as their small size, high structural flexi...

Reviewing on AI-Designed Antibiotic Targeting Drug-Resistant Superbugs by Emphasizing Mechanisms of Action.

Drug development research
The emergence of drug-resistant bacteria, often referred to as "superbugs," poses a profound and escalating challenge to global health systems, surpassing the capabilities of traditional antibiotic discovery methods. As resistance mechanisms evolve r...

Rational design and synthesis of pyrazole derivatives as potential SARS-CoV-2 M inhibitors: An integrated approach merging combinatorial chemistry, molecular docking, and deep learning.

Bioorganic & medicinal chemistry
The global impact of SARS-CoV-2 has highlighted the urgent need for novel antiviral therapies. This study integrates combinatorial chemistry, molecular docking, and deep learning to design, evaluate and synthesize new pyrazole derivatives as potentia...

Deep Learning Combined with Quantitative Structure‒Activity Relationship Accelerates De Novo Design of Antifungal Peptides.

Advanced science (Weinheim, Baden-Wurttemberg, Germany)
Novel antifungal drugs that evade resistance are urgently needed for Candida infections. Antifungal peptides (AFPs) are potential candidates due to their specific mechanism of action, which makes them less prone to developing drug resistance. An AFP ...

Deep-Learning-Based Approaches for Rational Design of Stapled Peptides With High Antimicrobial Activity and Stability.

Microbial biotechnology
Antimicrobial peptides (AMPs) face stability and toxicity challenges in clinical use. Stapled modification enhances their stability and effectiveness, but its application in peptide design is rarely reported. This study built ten prediction models fo...