AIMC Topic: Drug Discovery

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Artificial Intelligence-Driven Discovery of Pyrazolo[1,5-]pyrimidine Derivatives as Novel Phosphodiesterase 4 Inhibitors for Treating Idiopathic Pulmonary Fibrosis.

Journal of medicinal chemistry
Phosphodiesterase 4 (PDE4) has been validated as a promising therapeutic target for idiopathic pulmonary fibrosis (IPF), a devastating interstitial lung disease lacking really effective therapeutic drugs, particularly exacerbated in the post-COVID-19...

Next-generation antifungal peptide discovery: the synergy of artificial intelligence and omics technologies.

World journal of microbiology & biotechnology
There is a growing concern about fungal infections and antifungal resistance among fungal species, underscoring the need for finding alternative treatments. Antifungal peptides (AFPs) are interesting and promising candidates for developing novel anti...

Fungal virulence factors datasets for inflammatory bowel disease-specific antifungal drug discovery.

Scientific data
Fungi are closely associated with various diseases, among which Candida albicans (C. albicans) is recognized as an important pathogen in inflammatory bowel disease (IBD). Fungal pathogenicity is primarily mediated by virulence factors (VFs); therefor...

A Review of Topological Data Analysis and Topological Deep Learning in Molecular Sciences.

Journal of chemical information and modeling
Topological data analysis (TDA) has emerged as a powerful framework for extracting robust, multiscale, and interpretable features from complex molecular data for artificial intelligence (AI) modeling and topological deep learning (TDL). This review p...

How Feasible Is Docking of PROTACs to POI-E3L Complexes? Testing Physics-Based and ML-Based Docking Tools.

Journal of chemical information and modeling
Targeted protein degradation (TPD) is an innovative drug discovery approach that leverages small molecules to induce proximity between a protein of interest (POI) and an E3 ubiquitin ligase (E3L) for selective degradation. Among TPD modalities, prote...

Multi-stage variational autoencoders for hierarchical molecular generation and activity optimization.

Journal of computer-aided molecular design
Deep generative models may detect novel compounds with favourable features, exhibiting chemical design potential. Traditional single-stage variational autoencoders (VAEs) lack validity, uniqueness, and biologically meaningful distribution alignment. ...

MSformer: A Meta-Structure Based Interpretable Framework for Representation Learning of Natural Products.

Analytical chemistry
Natural products (NPs) are a treasure trove of drug discovery, yet their structural complexity and extreme data scarcity critically hinder AI-driven exploration. To address this challenge, we present MSformer, a transformer-based architecture that br...

The Use of DeepQSAR Models for the Discovery of Peptides with Enhanced Antimicrobial and Antibiofilm Potential.

Journal of chemical information and modeling
Increasing concerns regarding prolonged antibiotic usage have spurred the search for alternative treatments. Antimicrobial peptides (AMPs), first discovered in the 1980s, have exhibited significant potential against a broad range of bacteria. Short-s...

AGDNGDA: Unraveling Drug-Associated Genes with Adaptive Graph Diffusion Networks.

Journal of chemical information and modeling
Understanding the intricate relationships between genes and drugs is crucial for advancing drug discovery. However, biological experiments aimed at identifying gene-drug associations are typically time-consuming and inefficient, leading to significan...

Mycobacterium tuberculosis FAS-II pathway targeted integrative deep learning based identification of potential anti-tubercular agents.

Journal of computer-aided molecular design
Mycobacterium tuberculosis (Mtb) continues to be one of the major contributors to the global burden of infectious diseases. Many drugs used in the current treatment regime have fallen prey to the puzzling phenomenon of antimicrobial resistance. Despi...