AIMC Topic: Small Molecule Libraries

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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...

Descriptor-First Approach for ADMET Prediction in the PolarisHub Antiviral Challenge.

Journal of chemical information and modeling
The prediction of absorption, distribution, metabolism, excretion, and toxicity (ADMET) properties remains a central bottleneck in small-molecule discovery. We present the third-place solution from the PolarisHub Antiviral Competition, covering five ...

AttMVGraph: Attention-Based Multimodal Fusion and Variational Graph Learning for SM-miRNA Association Prediction.

Journal of chemical information and modeling
MiRNA serves as a key noncoding RNA regulating gene expression and is frequently targeted as a therapeutic small molecule (SM). However, relying solely on the experimental identification of SM-miRNA interactions proves costly and inefficient. To addr...

DeepTargetClass: a web-based platform for predicting protein target classes of small molecules.

Journal of computer-aided molecular design
The identification of protein target classes is a key step in drug discovery, as it enables prioritization of screening campaigns and supports target-based drug repurpose. In this study, we developed a deep-learning pipeline based on a multilayer per...

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, ...

CANDID-CNS: AI Unlocks Stereochemistry and Beyond Rule of 5 to Predict CNS Penetration of Small Molecules.

Journal of chemical information and modeling
Neuroscience is the most difficult therapeutic area for pharmaceutical drug discovery. The blood-brain barrier (BBB) prevents ∼100% of large molecules and >98% of small molecules from penetrating the central nervous system (CNS). Most small molecule ...

Modeling protein-small molecule conformational ensembles with PLACER.

Proceedings of the National Academy of Sciences of the United States of America
Modeling the conformational heterogeneity of protein-small molecule interactions is important for understanding natural systems and evaluating designed systems but remains an outstanding challenge. We reasoned that while residue-level descriptions of...

Machine Learning Guided by Physicochemical Principles Enables Generalized Prediction of Small-Molecule Subcellular Localization and Discovery of Targeted Molecules.

Analytical chemistry
Precise subcellular localization is crucial for the design of molecular probes and targeted therapeutics, yet selectively distinguishing organelles with similar physicochemical properties, such as lipid droplets, mitochondria, and the cell membrane, ...

4-Hydroxy-2,5-dihydrothiazole derivatives as a new class of small-molecule antibiotics for MRSA: AI-integrated design, chemical synthesis and biological evaluation.

European journal of medicinal chemistry
Staphylococcus aureus (S. aureus) is one of the most concerned Gram-positive bacteria due to its resistance to the commonly used antibiotics, methicillin. To address the threat of methicillin-resistant S. aureus (MRSA), new classes of antibiotics are...

ParametrizANI: Fast and Accessible Dihedral Parametrization for Small Molecules.

Journal of chemical information and modeling
In molecular studies, the accurate parametrization of small molecules stands as an essential yet growing demand. Addressing this, we introduce ParametrizANI, a tool crafted explicitly for establishing detailed protocols for dihedral parametrization u...