AIMC Topic: Small Molecule Libraries
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Emerging structure-based computational methods to screen the exploding accessible chemical space.
Current opinion in structural biology
Apr 10, 2024
Structure-based virtual screening can be a valuable approach to computationally select hit candidates based on their predicted interaction with a protein of interest. The recent explosion in the size of chemical libraries increases the chances of hit...
MolProphet: A One-Stop, General Purpose, and AI-Based Platform for the Early Stages of Drug Discovery.
Journal of chemical information and modeling
Apr 2, 2024
Artificial intelligence (AI) is an effective tool to accelerate drug discovery and cut costs in discovery processes. Many successful AI applications are reported in the early stages of small molecule drug discovery. However, most of those application...
Predicting Elimination of Small-Molecule Drug Half-Life in Pharmacokinetics Using Ensemble and Consensus Machine Learning Methods.
Journal of chemical information and modeling
Apr 2, 2024
Half-life is a significant pharmacokinetic parameter included in the excretion phase of absorption, distribution, metabolism, and excretion. It is one of the key factors for the successful marketing of drug candidates. Therefore, predicting half-life...
MLASM: Machine learning based prediction of anticancer small molecules.
Molecular diversity
Mar 30, 2024
Cancer, being the second leading cause of death globally. So, the development of effective anticancer treatments is crucial in the field of medicine. Anticancer peptides (ACPs) have shown promising therapeutic potential in cancer treatment compared t...
Target-specific novel molecules with their recipe: Incorporating synthesizability in the design process.
Journal of molecular graphics & modelling
Feb 28, 2024
Application of Artificial intelligence (AI) in drug discovery has led to several success stories in recent times. While traditional methods mostly relied upon screening large chemical libraries for early-stage drug-design, de novo design can help ide...
Recent applications of artificial intelligence in RNA-targeted small molecule drug discovery.
Expert opinion on drug discovery
Feb 6, 2024
INTRODUCTION: Targeting RNAs with small molecules offers an alternative to the conventional protein-targeted drug discovery and can potentially address unmet and emerging medical needs. The recent rise of interest in the strategy has already resulted...
Exploring the potential of machine learning to design antidiabetic molecules: a comprehensive study with experimental validation.
Journal of biomolecular structure & dynamics
Nov 8, 2023
Recent advances in hardware and software algorithms have led to the rise of data-driven approaches for designing therapeutic modalities. One of the major causes of human mortality is diabetes. Thus, there is a tremendous opportunity for research into...
Graph neural networks for the identification of novel inhibitors of a small RNA.
SLAS discovery : advancing life sciences R & D
Oct 14, 2023
MicroRNAs (miRNAs) play a crucial role in post-transcriptional gene regulation and have been implicated in various diseases, including cancers and lung disease. In recent years, Graph Neural Networks (GNNs) have emerged as powerful tools for analyzin...
Development and validation of machine learning models for the prediction of SH-2 containing protein tyrosine phosphatase 2 inhibitors.
Molecular diversity
Aug 8, 2023
Discovery and development of a new drug to the market is a highly challenging and resource consuming process. Although, modern drug discovery technologies have enabled the rapid identification of lead compounds, translation of the lead compounds into...
Streamlining Large Chemical Library Docking with Artificial Intelligence: the PyRMD2Dock Approach.
Journal of chemical information and modeling
Aug 8, 2023
The present contribution introduces a novel computational protocol called PyRMD2Dock, which combines the Ligand-Based Virtual Screening (LBVS) tool PyRMD with the popular docking software AutoDock-GPU (AD4-GPU) to enhance the throughput of virtual sc...
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