AIMC Topic: Molecular Structure

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Recent research frontiers of heterocycles as antifungal Agents: Insights from the past five years.

European journal of medicinal chemistry
This review explores the growing global concern of fungal infections, particularly in immunocompromised individuals, and highlights the critical need for improved antifungal therapies. With the rise of multidrug-resistant strains, such as Candida aur...

Exploring microbial natural products through NMR-based metabolomics.

Natural product reports
Covering: 2000. 01 to 2025. 03The soaring demand for novel drugs has led to an increase in the requirement for smart methods to aid in the exploration of microbial natural products (NPs). Cutting-edge metabolomics excels at prompt identification of c...

First report on analysis of chemical space, scaffold diversity, critical structural features of HDAC11 inhibitors.

Molecular diversity
In the histone deacetylase (HDAC) family, HDAC11 is the smallest and a single member under the class IV subtype. It is important as a drug target mainly in cancer, inflammatory and autoimmune diseases. The design and development of selective HDAC11 i...

In silico discovery of novel compounds for FAK activation using virtual screening, AI-based prediction, and molecular dynamics.

Computational biology and chemistry
Focal Adhesion Kinase (FAK) is a non-receptor tyrosine kinase that plays a crucial role in cell proliferation, migration, and signal transduction. FAK is overexpressed in metastatic and advanced-stage cancers, where it is considered a key kinase in c...

Structural Bias in Three-Dimensional Autoregressive Generative Machine Learning of Organic Molecules.

Journal of chemical information and modeling
A range of generative machine learning models for the design of novel molecules and materials have been proposed in recent years. Models that can generate three-dimensional structures are particularly suitable for quantum chemistry workflows, enablin...

A deep learning model for structure-based bioactivity optimization and its application in the bioactivity optimization of a SARS-CoV-2 main protease inhibitor.

European journal of medicinal chemistry
Bioactivity optimization is a crucial and technical task in the early stages of drug discovery, traditionally carried out through iterative substituent optimization, a process that is often both time-consuming and expensive. To address this challenge...

Discovery of naturally inspired antimicrobial peptides using deep learning.

Bioorganic chemistry
Non-ribosomal peptides (NRPs) are promising lead compounds for novel antibiotics. Bioinformatic mining of silent microbial NRPS gene clusters provide crucial insights for the discovery and de novo design of bioactive peptides. Here, we describe the e...

Accurate Identification of MDMB-Type Synthetic Cannabinoids through Design of Dual Excited-State Intramolecular Proton Transfer Site Probe and Deep-Learning.

Analytical chemistry
Synthetic cannabinoids, a novel class of highly toxic psychoactive substances with various disguised forms, have posed significant risks to public safety, and their weak reactivity presents a substantial challenge for swift and accurate analysis. In ...

Machine learning-based bioactivity prediction of porphyrin derivatives: molecular descriptors, clustering, and model evaluation.

Photochemical & photobiological sciences : Official journal of the European Photochemistry Association and the European Society for Photobiology
Understanding the relationship between molecular structure and bioactivity is crucial for optimizing porphyrin-based therapeutics. By integrating cheminformatics techniques with machine learning models, our work enables the efficient classification o...

Machine Learning for Toxicity Prediction Using Chemical Structures: Pillars for Success in the Real World.

Chemical research in toxicology
Machine learning (ML) is increasingly valuable for predicting molecular properties and toxicity in drug discovery. However, toxicity-related end points have always been challenging to evaluate experimentally with respect to translation due to the re...