AIMC Topic: Drug Discovery
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Enhancing Generalizability in Protein-Ligand Binding Affinity Prediction with Multimodal Contrastive Learning.
Journal of chemical information and modeling
Mar 5, 2024
Improving the generalization ability of scoring functions remains a major challenge in protein-ligand binding affinity prediction. Many machine learning methods are limited by their reliance on single-modal representations, hindering a comprehensive ...
Drug discovery and development: the role of artificial intelligence in drug repurposing.
Future medicinal chemistry
Mar 1, 2024
Computational discovery of novel FYN kinase inhibitors: a cheminformatics and machine learning-driven approach to targeted cancer and neurodegenerative therapy.
Molecular diversity
Feb 28, 2024
In this study, we explored the potential of novel inhibitors for FYN kinase, a critical target in cancer and neurodegenerative disorders, by integrating advanced cheminformatics, machine learning, and molecular simulation techniques. Our approach inv...
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...
PandaOmics: An AI-Driven Platform for Therapeutic Target and Biomarker Discovery.
Journal of chemical information and modeling
Feb 25, 2024
PandaOmics is a cloud-based software platform that applies artificial intelligence and bioinformatics techniques to multimodal omics and biomedical text data for therapeutic target and biomarker discovery. PandaOmics generates novel and repurposed th...
Kinome-Wide Virtual Screening by Multi-Task Deep Learning.
International journal of molecular sciences
Feb 22, 2024
Deep learning is a machine learning technique to model high-level abstractions in data by utilizing a graph composed of multiple processing layers that experience various linear and non-linear transformations. This technique has been shown to perform...
Insights into artificial intelligence utilisation in drug discovery.
Journal of medical economics
Feb 22, 2024
Enhancing drug discovery in schizophrenia: a deep learning approach for accurate drug-target interaction prediction - DrugSchizoNet.
Computer methods in biomechanics and biomedical engineering
Feb 20, 2024
Drug discovery relies on the precise prognosis of drug-target interactions (DTI). Due to their ability to learn from raw data, deep learning (DL) methods have displayed outstanding performance over traditional approaches. However, challenges such as ...
Adapting Deep Learning QSPR Models to Specific Drug Discovery Projects.
Molecular pharmaceutics
Feb 19, 2024
Medicinal chemistry and drug design efforts can be assisted by machine learning (ML) models that relate the molecular structure to compound properties. Such quantitative structure-property relationship models are generally trained on large data sets ...
BioPrint meets the AI age: development of artificial intelligence-based ADMET models for the drug-discovery platform SAFIRE.
Future medicinal chemistry
Feb 19, 2024
To prioritize compounds with a higher likelihood of success, artificial intelligence models can be used to predict absorption, distribution, metabolism, excretion and toxicity (ADMET) properties of molecules quickly and efficiently. Models were tra...
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