AIMC Topic: Drug Design

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Peptide-based drug design using generative AI.

Chemical communications (Cambridge, England)
Peptide-based therapeutics have emerged as a significant treatment strategy, offering high specificity and tunable pharmacokinetics. Recent advances in Artificial Intelligence (AI) have shifted the focus towards structure prediction, generative desig...

Prodrug-ML: prodrug-likeness prediction via machine learning on sampled negative decoys.

Journal of computer-aided molecular design
A prodrug is a pharmacologically inactive (or attenuated) derivative that undergoes bioreversible transformation in vivo to release an active parent drug, enabling temporary optimization of properties such as solubility, permeability, and targeting. ...

AI-driven molecular modeling and design: from property prediction to drug generation.

Journal of computer-aided molecular design
Integrating the techniques of deep learning, particularly graph neural network models, has made a significant advancement in drug discovery by facilitating effective exploration of chemical spaces and precise prediction of molecular properties. This ...

Design of Highly Potent Antibiofilm, Antimicrobial Peptides Using Explainable Artificial Intelligence.

Journal of chemical information and modeling
Antimicrobial peptides have emerged as a potential alternative to traditional small-molecule antimicrobials. They possess broad-spectrum efficacy and increasingly confront the challenges of bacterial resistance, especially the adaptive resistance of ...

In silico-driven protocol for hit-to-lead optimization: a case study on PDE9A inhibitors.

Journal of computer-aided molecular design
Hit-to-lead (H2L) optimization is a critical stage in small-molecule drug discovery, where efficient exploration of chemical space is required to identify promising lead compounds. Conventional H2L workflows rely on iterative synthesis and experiment...

mRNA-LNP vaccines: rational design, delivery optimization, and clinical translation.

Journal of materials chemistry. B
Messenger RNA (mRNA) vaccines face core challenges including low-delivery efficiency and immunogenicity, limiting their wide-ranging applications in infectious disease prevention and cancer therapy. Lipid nanoparticles (LNPs), the most clinically val...

Uncertainty quantification enables reliable deep learning for protein-ligand binding affinity prediction.

Scientific reports
Deep learning (DL) algorithms have increasingly been applied to predict protein-ligand binding affinity, a critical step in drug design. Yet, many models still struggle to generalize to unseen data, and when coupled with the absence of confidence est...

pKa prediction for small molecules: an overview of experimental, quantum, and machine learning-based approaches.

Journal of computer-aided molecular design
The pKa, also known as the logarithmic dissociation constant, is a crucial parameter that defines the ionization level of a molecule when it is in solution. It is essential for several physicochemical properties, including lipophilicity, solubility, ...

Toxicity assessment of doxycycline-aided artificial intelligence-assisted drug design targeting candidate 16S rRNA methyltransferase gene.

BMC pharmacology & toxicology
BACKGROUND: The misfunction of the protein 16SrRNA methyltransferase can result in Urinary tract infections (UTI), Gastrointestinal (GI) infections, sepsis, pneumonia, and wound infections; various tactics are used to lessen the fatal consequences. I...

Generative AI for the Design of Molecules: Advances and Challenges.

Journal of chemical information and modeling
The design of novel molecules underpins advances in both drug discovery and biomaterials engineering. Traditional approaches, from natural product isolation to high-throughput screening, have delivered important therapeutics but remain costly, ineffi...