AI Medical Compendium Journal:
Drug discovery today

Showing 31 to 40 of 107 articles

Transfer learning empowers accurate pharmacokinetics prediction of small samples.

Drug discovery today
Accurate assessment of pharmacokinetic (PK) properties is crucial for selecting optimal candidates and avoiding downstream failures. Transfer learning is an innovative machine learning approach enabling high-throughput prediction with limited data. R...

Augmenting DMTA using predictive AI modelling at AstraZeneca.

Drug discovery today
Design-Make-Test-Analyse (DMTA) is the discovery cycle through which molecules are designed, synthesised, and assayed to produce data that in turn are analysed to inform the next iteration. The process is repeated until viable drug candidates are ide...

Multiorgan locked-state model of chronic diseases and systems pharmacology opportunities.

Drug discovery today
With increasing human life expectancy, the global medical burden of chronic diseases is growing. Hence, chronic diseases are a pressing health concern and will continue to be in decades to come. Chronic diseases often involve multiple malfunctioning ...

Artificial intelligence methods in kinase target profiling: Advances and challenges.

Drug discovery today
Kinases have a crucial role in regulating almost the full range of cellular processes, making them essential targets for therapeutic interventions against various diseases. Accurate kinase-profiling prediction is vital for addressing the selectivity/...

Drug Intelligence Science (DISĀ®): Pioneering a high-resolution translational platform to enhance the probability of success for drug discovery and development.

Drug discovery today
Translational research has a crucial role in bridging the gap between basic biology discoveries and their clinical applications. Deep scientific understanding and advanced technology platforms are both crucial for translational research. Here, I desc...

Transforming drug discovery with a high-throughput AI-powered platform: A 5-year experience with Patrimony.

Drug discovery today
High-throughput computational platforms are being established to accelerate drug discovery. Servier launched the Patrimony platform to harness computational sciences and artificial intelligence (AI) to integrate massive multimodal data from internal ...

In silico co-crystal design: Assessment of the latest advances.

Drug discovery today
Pharmaceutical co-crystals represent a growing class of crystal forms in the context of pharmaceutical science. They are attractive to pharmaceutical scientists because they significantly expand the number of crystal forms that exist for an active ph...

Artificial intelligence in pharmaceutical regulatory affairs.

Drug discovery today
Artificial intelligence (AI) refers to the ability of a computer to carry out tasks associated with human intelligence, including thinking, discovering, and learning from prior experience. AI can be integrated to simplify the complexity of pharmaceut...

Quantum computing for near-term applications in generative chemistry and drug discovery.

Drug discovery today
In recent years, drug discovery and life sciences have been revolutionized with machine learning and artificial intelligence (AI) methods. Quantum computing is touted to be the next most significant leap in technology; one of the main early practical...

The recent progress of deep-learning-based in silico prediction of drug combination.

Drug discovery today
Drug combination therapy has become a common strategy for the treatment of complex diseases. There is an urgent need for computational methods to efficiently identify appropriate drug combinations owing to the high cost of experimental screening. In ...