AIMC Topic: Drug Design

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AI-designed PNA-peptide chimera overcomes suboptimal binding for dual inhibition of viral RdRp.

European journal of medicinal chemistry
The chimera combining the peptide nucleic acids (PNAs) and peptides represent a promising bifunctional strategy by concurrently binding with protein catalytic pocket and its associated RNA template, effectively disrupting protein's function. Conventi...

Selectivity Approaches in Therapeutic Antibody Design.

Journal of medicinal chemistry
Protein therapeutics, particularly antibody-based therapies, have emerged as a cornerstone in modern disease treatment, offering key advantages over small molecules, including superior target specificity, longer half-life, and expanded target accessi...

RAPID-Net: Accurate Pocket Identification for Binding-Site-Agnostic Docking.

Journal of chemical information and modeling
Accurate identification of druggable pockets and their features is essential for structure-based drug design and effective downstream docking. Here, we present RAPID-Net, a deep learning-based algorithm designed for accurate prediction of binding poc...

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. ...

Design of Carbon Nanotube Inhibitors for Main Proteinase of SARS-CoV-2: A Combined Deep Learning and Molecular Dynamics Simulation Study.

The journal of physical chemistry. B
The rapid development of machine learning (ML) and deep learning (DL) methods provides new opportunities for innovative drug discovery. While these techniques are widely used in docking organic molecules (drugs) with protein, an evaluation of the per...

RLMolLM: Reinforcement Learning-Enhanced Language Model Framework for Inverse Molecular Design.

Journal of chemical information and modeling
Inverse molecular design faces significant challenges due to vast chemical space and complex property requirements. While language models show promise for molecular generation, they struggle with validity, multi-property optimization, and structural ...

Machine Learning-Based Bioactivity Prediction and Descriptor-Guided Rational Design of Amyloid-β Aggregation Inhibitors.

ACS chemical neuroscience
Alzheimer's disease (AD) is a progressive neurodegenerative disorder in which amyloid-β (Aβ) aggregation plays a pivotal role in its onset and progression. Inhibiting Aβ aggregation is a promising therapeutic strategy; however, its intrinsically diso...

Design, synthesis, deep learning-guided prediction, and biological evaluation of novel pyridine-thiophene-based imine-benzalacetophenone hybrids as promising antimicrobial agent.

Journal of computer-aided molecular design
Antimicrobial resistance (AMR) remains a global health crisis, necessitating the development of novel therapeutics against multidrug-resistant pathogens. In this study, ten (10) hybrid imine-benzalacetophenone derivatives (7a-7j), incorporating pyrid...

Multistep Machine Learning Pipeline For Polymeric Nanoparticle Design.

AAPS PharmSciTech
Integrating machine learning (ML) into nanotechnology represents a promising strategy for rational design and accelerated development of drug delivery systems. However, studies in this field are scarce and face methodological and interpretative probl...

Design of Specific Peptide Inhibitors of Toxin-Antitoxin-Mediated Antiphage Defense.

ACS synthetic biology
Toxin-antitoxin (TA) systems are widespread antiphage defense elements in bacteria that may impede successful phage therapy. Phage-encoded inhibitors of these systems have been discovered that enhance phage infection capacity. We used fragment-based ...