AIMC Topic: Drug Design

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Linker-GPT: design of Antibody-drug conjugates linkers with molecular generators and reinforcement learning.

Scientific reports
The stability and therapeutic efficacy of antibody-drug conjugates (ADCs) are critically determined by the chemical linkers that connect the antibody to the cytotoxic payload, which is a key factor influencing drug release, plasma stability, and off-...

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 ...

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...

Artificial intelligence revolution in drug discovery: A paradigm shift in pharmaceutical innovation.

International journal of pharmaceutics
Integrating artificial intelligence (AI) into drug discovery has revolutionized pharmaceutical innovation, addressing the challenges of traditional methods that are costly, time-consuming, and suffer from high failure rates. By utilizing machine lear...

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...

Role of artificial intelligence in cancer drug discovery and development.

Cancer letters
The role of artificial intelligence (AI) in cancer drug discovery and development has garnered significant attention due to its potential to transform the traditionally time-consuming and expensive processes involved in bringing new therapies to mark...

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...