AIMC Topic: Drug Discovery

Clear Filters Showing 541 to 550 of 1689 articles

Target-specific novel molecules with their recipe: Incorporating synthesizability in the design process.

Journal of molecular graphics & modelling
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
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
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...

Enhancing drug discovery in schizophrenia: a deep learning approach for accurate drug-target interaction prediction - DrugSchizoNet.

Computer methods in biomechanics and biomedical engineering
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
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
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...

Recent advances in generative biology for biotherapeutic discovery.

Trends in pharmacological sciences
Generative biology combines artificial intelligence (AI), advanced life sciences technologies, and automation to revolutionize the process of designing novel biomolecules with prescribed properties, giving drug discoverers the ability to escape the l...

Machine Learning Empowering Drug Discovery: Applications, Opportunities and Challenges.

Molecules (Basel, Switzerland)
Drug discovery plays a critical role in advancing human health by developing new medications and treatments to combat diseases. How to accelerate the pace and reduce the costs of new drug discovery has long been a key concern for the pharmaceutical i...

graphLambda: Fusion Graph Neural Networks for Binding Affinity Prediction.

Journal of chemical information and modeling
Predicting the binding affinity of protein-ligand complexes is crucial for computer-aided drug discovery (CADD) and the identification of potential drug candidates. The deep learning-based scoring functions have emerged as promising predictors of bin...