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Machine learning in preclinical drug discovery
Drug-discovery and drug-development endeavors are laborious, costly and time consuming.
These programs can take upward of 12 years and cost US $2.5 billion, with a failure rate of …
These programs can take upward of 12 years and cost US $2.5 billion, with a failure rate of …
Unlocking the potential of generative AI in drug discovery
Highlights•Artificial intelligence (AI) is transforming the drug discovery process by providing
actionable insights from huge amount of data.•Deep-learning models, especially generative …
actionable insights from huge amount of data.•Deep-learning models, especially generative …
Current strategies to address data scarcity in artificial intelligence-based drug discovery: A comprehensive review
Artificial intelligence (AI) has played a vital role in computer-aided drug design (CADD). This
development has been further accelerated with the increasing use of machine learning (ML) …
development has been further accelerated with the increasing use of machine learning (ML) …
Tackling assay interference associated with small molecules
Biochemical and cell-based assays are essential to discovering and optimizing efficacious
and safe drugs, agrochemicals and cosmetics. However, false assay readouts stemming …
and safe drugs, agrochemicals and cosmetics. However, false assay readouts stemming …
Industry-scale orchestrated federated learning for drug discovery
To apply federated learning to drug discovery we developed a novel platform in the context
of European Innovative Medicines Initiative (IMI) project MELLODDY (grant n 831472) …
of European Innovative Medicines Initiative (IMI) project MELLODDY (grant n 831472) …
Deep learning models compared to experimental variability for the prediction of CYP3A4 time-dependent inhibition
Most drugs are mainly metabolized by cytochrome P450 (CYP450), which can lead to drug–
drug interactions (DDI). Specifically, time-dependent inhibition (TDI) of CYP3A4 isoenzyme …
drug interactions (DDI). Specifically, time-dependent inhibition (TDI) of CYP3A4 isoenzyme …
Recent advances in generative biology for biotherapeutic discovery
Generative biology combines artificial intelligence (AI), advanced life sciences technologies,
and automation to revolutionize the process of designing novel biomolecules with …
and automation to revolutionize the process of designing novel biomolecules with …
Prediction of Small-Molecule Developability Using Large-Scale In Silico ADMET Models
Early in silico assessment of the potential of a series of compounds to deliver a drug is one
of the major challenges in computer-assisted drug design. The goal is to identify the right …
of the major challenges in computer-assisted drug design. The goal is to identify the right …
Multi-party collaborative drug discovery via federated learning
In the field of drug discovery and pharmacology research, precise and rapid prediction of
drug-target binding affinity (DTA) and drug-drug interaction (DDI) are essential for drug …
drug-target binding affinity (DTA) and drug-drug interaction (DDI) are essential for drug …
Another string to your bow: Machine learning prediction of the pharmacokinetic properties of small molecules
Introduction Prediction of pharmacokinetic (PK) properties is crucial for drug discovery and
development. Machine-learning (ML) models, which use statistical pattern recognition to …
development. Machine-learning (ML) models, which use statistical pattern recognition to …