AIMC Topic: Structure-Activity Relationship
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Synthesis, Docking, and Machine Learning Studies of Some Novel Quinolinesulfonamides-Triazole Hybrids with Anticancer Activity.
Molecules (Basel, Switzerland)
Jul 2, 2024
In the presented work, a series of 22 hybrids of 8-quinolinesulfonamide and 1,4-disubstituted triazole with antiproliferative activity were designed and synthesised. The title compounds were designed using molecular modelling techniques. For this pur...
PepExplainer: An explainable deep learning model for selection-based macrocyclic peptide bioactivity prediction and optimization.
European journal of medicinal chemistry
Jun 25, 2024
Macrocyclic peptides possess unique features, making them highly promising as a drug modality. However, evaluating their bioactivity through wet lab experiments is generally resource-intensive and time-consuming. Despite advancements in artificial in...
From Deep Learning to the Discovery of Promising VEGFR-2 Inhibitors.
ChemMedChem
Jun 20, 2024
Vascular endothelial growth factor receptor 2 (VEGFR-2) stands as a prominent therapeutic target in oncology, playing a critical role in angiogenesis, tumor growth, and metastasis. FDA-approved VEGFR-2 inhibitors are associated with diverse side effe...
Drug Discovery
Antineoplastic Agents
Deep Learning
Humans
Molecular Docking Simulation
Vascular Endothelial Growth Factor Receptor-2
Protein Kinase Inhibitors
Drug Screening Assays, Antitumor
Structure-Activity Relationship
Molecular Structure
Cell Line, Tumor
Cell Proliferation
Dose-Response Relationship, Drug
Task-Similarity is a Crucial Factor for Few-Shot Meta-Learning of Structure-Activity Relationships.
Chembiochem : a European journal of chemical biology
Jun 18, 2024
Machine learning models support computer-aided molecular design and compound optimization. However, the initial phases of drug discovery often face a scarcity of training data for these models. Meta-learning has emerged as a potentially promising str...
Synthetically Feasible De Novo Molecular Design of Leads Based on a Reinforcement Learning Model: AI-Assisted Discovery of an Anti-IBD Lead Targeting CXCR4.
Journal of medicinal chemistry
Jun 12, 2024
Artificial intelligence (AI) de novo molecular generation provides leads with novel structures for drug discovery. However, the target affinity and synthesizability of the generated molecules present critical challenges for the successful application...
Artificial Intelligence-Assisted Optimization of Antipigmentation Tyrosinase Inhibitors: Molecular Generation Based on a Low Activity Lead Compound.
Journal of medicinal chemistry
Apr 23, 2024
Artificial intelligence (AI) molecular generation is a highly promising strategy in the drug discovery, with deep reinforcement learning (RL) models emerging as powerful tools. This study introduces a fragment-by-fragment growth RL forward molecular...
Discovery of potent inhibitors of α-synuclein aggregation using structure-based iterative learning.
Nature chemical biology
Apr 17, 2024
Machine learning methods hold the promise to reduce the costs and the failure rates of conventional drug discovery pipelines. This issue is especially pressing for neurodegenerative diseases, where the development of disease-modifying drugs has been ...
An Artificial Intelligence-Supported Medicinal Chemistry Project: An Example for Incorporating Artificial Intelligence Within the Pharmacy Curriculum.
American journal of pharmaceutical education
Apr 2, 2024
OBJECTIVE: This study aims to integrate and use AI to teach core concepts in a medicinal chemistry course and to increase the familiarity of pharmacy students with AI in pharmacy practice and drug development. Artificial intelligence (AI) is a multid...
Machine learning and genetic algorithm-guided directed evolution for the development of antimicrobial peptides.
Journal of advanced research
Mar 1, 2024
INTRODUCTION: Antimicrobial peptides (AMPs) are valuable alternatives to traditional antibiotics, possess a variety of potent biological activities and exhibit immunomodulatory effects that alleviate difficult-to-treat infections. Clarifying the stru...
Deep learning for advancing peptide drug development: Tools and methods in structure prediction and design.
European journal of medicinal chemistry
Feb 19, 2024
Peptides can bind challenging disease targets with high affinity and specificity, offering enormous opportunities for addressing unmet medical needs. However, peptides' unique features, including smaller size, increased structural flexibility, and li...
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