AIMC Topic: Structure-Activity Relationship
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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...
Using deep learning and molecular dynamics simulations to unravel the regulation mechanism of peptides as noncompetitive inhibitor of xanthine oxidase.
Scientific reports
Jan 2, 2024
Xanthine oxidase (XO) is a crucial enzyme in the development of hyperuricemia and gout. This study focuses on LWM and ALPM, two food-derived inhibitors of XO. We used molecular docking to obtain three systems and then conducted 200 ns molecular dynam...
Deep learning-based design and screening of benzimidazole-pyrazine derivatives as adenosine A receptor antagonists.
Journal of biomolecular structure & dynamics
Dec 22, 2023
The Adenosine A receptor (AAR) is considered a novel potential target for the immunotherapy of cancer, and AAR antagonists have an inhibitory effect on tumor growth, proliferation, and metastasis. In our previous studies, we identified a class of ben...
1,3,4-oxadiazole derivatives: synthesis, characterization, antifungal activity, DNA binding investigations, TD-DFT calculations, and molecular modelling.
Journal of biomolecular structure & dynamics
Dec 21, 2023
1,3,4-Oxadiazole-based heterocyclic analogs (3a-3m) were synthesized cyclization of Schiff bases with substituted aldehydes in the presence of bromine and acetic acid. The structural clarification of synthesized molecules was carried out with variou...
Identification of active compounds as novel dipeptidyl peptidase-4 inhibitors through machine learning and structure-based molecular docking simulations.
Journal of biomolecular structure & dynamics
Dec 15, 2023
The enzyme dipeptidyl peptidase 4 (DPP4) is a potential therapeutic target for type 2 diabetes (T2DM). Many synthetic anti-DPP4 medications are available to treat T2DM. The need for secure and efficient medicines has been unmet due to the adverse sid...
Anatomy of Potency Predictions Focusing on Structural Analogues with Increasing Potency Differences Including Activity Cliffs.
Journal of chemical information and modeling
Nov 9, 2023
Potency predictions are popular in compound design and optimization but are complicated by intrinsic limitations. Moreover, even for nonlinear methods, activity cliffs (ACs, formed by structural analogues with large potency differences) represent cha...
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