AIMC Topic: Drug Development

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

International journal of pharmaceutics Feb 2, 2021
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...
Humans Pharmaceutical Preparations Solubility Drug Compounding Artificial Intelligence Spectrum Analysis, Raman Drug Development
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[Applications of artificial intelligence to new drug development].

Annales pharmaceutiques francaises Jan 30, 2021
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...
Humans Artificial Intelligence Precision Medicine Drug Development
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Use of artificial intelligence to enhance phenotypic drug discovery.

Drug discovery today Jan 20, 2021
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...
Molecular Targeted Therapy Phenomics Machine Learning Drug Discovery Drug Development Humans Artificial Intelligence
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Applications of artificial intelligence in drug development using real-world data.

Drug discovery today Dec 24, 2020
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...
Drug Development Drug Repositioning United States Food and Drug Administration Animals Deep Learning United States Artificial Intelligence Machine Learning Humans
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Assessing Drug Development Risk Using Big Data and Machine Learning.

Cancer research Dec 22, 2020
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 ...
Male Risk Assessment Neoplasms Female Drug Delivery Systems Drug-Related Side Effects and Adverse Reactions Antineoplastic Agents Big Data Drug Development Humans Machine Learning Patient Safety
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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 Dec 17, 2020
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 ...
Humans Artificial Intelligence Drug Development Animals Decision Making Drug Discovery Clinical Trials as Topic
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Target-Centered Drug Repurposing Predictions of Human Angiotensin-Converting Enzyme 2 (ACE2) and Transmembrane Protease Serine Subtype 2 (TMPRSS2) Interacting Approved Drugs for Coronavirus Disease 2019 (COVID-19) Treatment through a Drug-Target Interaction Deep Learning Model.

Viruses Nov 18, 2020
Previously, our group predicted commercially available Food and Drug Administration (FDA) approved drugs that can inhibit each step of the replication of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) using a deep learning-based drug-ta...
COVID-19 Drug Treatment Drug Development Serine Endopeptidases Drug Repositioning Virus Replication Humans Angiotensin-Converting Enzyme 2 Hepacivirus SARS-CoV-2 Virus Internalization Deep Learning
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Network-based machine learning in colorectal and bladder organoid models predicts anti-cancer drug efficacy in patients.

Nature communications Oct 30, 2020
Cancer patient classification using predictive biomarkers for anti-cancer drug responses is essential for improving therapeutic outcomes. However, current machine-learning-based predictions of drug response often fail to identify robust translational...
Fluorouracil Transcriptome Colorectal Neoplasms Urinary Bladder Gene Regulatory Networks Humans Biomarkers, Tumor Urinary Bladder Neoplasms Cell Line, Tumor Organoids Machine Learning Gene Expression Regulation, Neoplastic Antineoplastic Agents Cisplatin Protein Interaction Maps Drug Development
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Ensemble transfer learning for the prediction of anti-cancer drug response.

Scientific reports Oct 22, 2020
Transfer learning, which transfers patterns learned on a source dataset to a related target dataset for constructing prediction models, has been shown effective in many applications. In this paper, we investigate whether transfer learning can be used...
Datasets as Topic Drug Repositioning Drug Screening Assays, Antitumor Models, Biological Deep Learning Precision Medicine Drug Development Neoplasms Humans Antineoplastic Agents Neural Networks, Computer Algorithms
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FPSC-DTI: drug-target interaction prediction based on feature projection fuzzy classification and super cluster fusion.

Molecular omics Oct 21, 2020
Identifying drug-target interactions (DTIs) is an important part of drug discovery and development. However, identifying DTIs is a complex process that is time consuming, costly, long, and often inefficient, with a low success rate, especially with w...
Algorithms Drug Development Fuzzy Logic Databases, Protein Area Under Curve
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