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Drug Development

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DTI-SNNFRA: Drug-target interaction prediction by shared nearest neighbors and fuzzy-rough approximation.

PloS one
In-silico prediction of repurposable drugs is an effective drug discovery strategy that supplements de-nevo drug discovery from scratch. Reduced development time, less cost and absence of severe side effects are significant advantages of using drug r...

Driving success in personalized medicine through AI-enabled computational modeling.

Drug discovery today
The development of successful drugs is expensive and time-consuming because of high clinical attrition rates. This is caused partially by the rupture seen in the translatability of the drug from the bench to the clinic in the context of personalized ...

Artificial intelligence, machine learning, and drug repurposing in cancer.

Expert opinion on drug discovery
: Drug repurposing provides a cost-effective strategy to re-use approved drugs for new medical indications. Several machine learning (ML) and artificial intelligence (AI) approaches have been developed for systematic identification of drug repurposin...

Potential of Raman spectroscopy in facilitating pharmaceutical formulations development - An AI perspective.

International journal of pharmaceutics
Drug development is time-consuming and inherently possesses a high failure rate. Pharmaceutical formulation development is the bridge that links a new chemical entity (NCE) to pre-clinical and clinical trials, and has a high impact on the efficacy an...

[Applications of artificial intelligence to new drug development].

Annales pharmaceutiques francaises
Artificial intelligence (AI) encompasses technologies recapitulating four dimensions of human intelligence, i.e. sensing, thinking, acting and learning. The convergence of technological advances in those fields allows to integrate massive data and bu...

Use of artificial intelligence to enhance phenotypic drug discovery.

Drug discovery today
Research and development (R&D) productivity across the pharmaceutical industry has received close scrutiny over the past two decades, especially taking into consideration reports of attrition rates and the colossal cost for drug development. The resp...

Applications of artificial intelligence in drug development using real-world data.

Drug discovery today
The US Food and Drug Administration (FDA) has been actively promoting the use of real-world data (RWD) in drug development. RWD can generate important real-world evidence reflecting the real-world clinical environment where the treatments are used. M...

Assessing Drug Development Risk Using Big Data and Machine Learning.

Cancer research
Identifying new drug targets and developing safe and effective drugs is both challenging and risky. Furthermore, characterizing drug development risk, the probability that a drug will eventually receive regulatory approval, has been notoriously hard ...

Artificial intelligence in drug discovery: what is realistic, what are illusions? Part 1: Ways to make an impact, and why we are not there yet.

Drug discovery today
Although artificial intelligence (AI) has had a profound impact on areas such as image recognition, comparable advances in drug discovery are rare. This article quantifies the stages of drug discovery in which improvements in the time taken, success ...