AIMC Topic: Drug Design

Clear Filters Showing 51 to 60 of 632 articles

Sequence and Structure-based Prediction of Allosteric Sites.

Journal of molecular biology
Allosteric regulation in proteins is a critical aspect of cellular function, influencing various biological processes through conformational or dynamic changes induced by effector molecules. Allosteric drugs possess significant therapeutic value due ...

Advancing active compound discovery for novel drug targets: insights from AI-driven approaches.

Acta pharmacologica Sinica
The discovery of active compounds for novel, underexplored targets is essential for advancing innovative therapeutics across a wide range of diseases. Recent advancements in artificial intelligence (AI) are revolutionizing active compound discovery b...

Quantum-inspired computational drug design for phytopharmaceuticals: a herbal holography analysis.

Journal of molecular modeling
CONTEXT: Modern medication discovery is undergoing a paradigm change at the junction of herbal pharmacology with computational modeling informed by quantum theory. Herbal compounds, which have often been considered as complex and poorly understood en...

Exploring the role of density functional theory in the design of gold nanoparticles for targeted drug delivery: a systematic review.

Journal of molecular modeling
CONTEXT: Targeted drug delivery systems leveraging gold nanoparticles (AuNPs) demand precise atomic-level design to overcome current limitations in drug-loading efficiency and controlled release. Unlike previous focused reviews, this systematic analy...

Improving Covalent and Noncovalent Molecule Generation via Reinforcement Learning with Functional Fragments.

Journal of chemical information and modeling
Small-molecule drugs play a critical role in cancer therapy by selectively targeting key signaling pathways that drive tumor growth. While deep learning models have advanced drug discovery, there remains a lack of generative frameworks for covalent ...

Computational design of therapeutic antibodies with improved developability: efficient traversal of binder landscapes and rescue of escape mutations.

mAbs
Developing therapeutic antibodies is a challenging endeavor, often requiring large-scale screening to produce initial binders, that still often require optimization for developability. We present a computational pipeline for the discovery and design ...

Molecular Optimization Based on a Monte Carlo Tree Search and Multiobjective Genetic Algorithm.

Journal of chemical information and modeling
In the realm of medicinal chemistry, the predominant challenge in molecular design lies in managing extensive molecular data sets and effectively screening for, as well as preserving, molecules with potential value. Traditional methodologies typicall...

AMPGen: an evolutionary information-reserved and diffusion-driven generative model for de novo design of antimicrobial peptides.

Communications biology
The rapid advancement of artificial intelligence (AI) has enabled de novo design of functional proteins, circumventing the reliance on natural templates or sequencing databases. However, current protein design models are ineffective in generating pro...

Oral ENPP1 inhibitor designed using generative AI as next generation STING modulator for solid tumors.

Nature communications
Despite the STING-type-I interferon pathway playing a key role in effective anti-tumor immunity, the therapeutic benefit of direct STING agonists appears limited. In this study, we use several artificial intelligence techniques and patient-based mult...

Design, synthesis, and evaluation of triazolo[1,5-a]pyridines as novel and potent α‑glucosidase inhibitors.

Scientific reports
α-Glucosidase is a key enzyme responsible for controlling the blood glucose, making a pivotal target in the treatment of type 2 diabetes mellitus. Present work introducestriazolo[1,5-a]pyridine as a novel, potent scaffold for α-glucosidase inhibition...