Artificial intelligence to deep learning: machine intelligence approach for drug discovery
Drug designing and development is an important area of research for pharmaceutical
companies and chemical scientists. However, low efficacy, off-target delivery, time …
companies and chemical scientists. However, low efficacy, off-target delivery, time …
[HTML][HTML] A review on machine learning approaches and trends in drug discovery
P Carracedo-Reboredo, J Liñares-Blanco… - Computational and …, 2021 - Elsevier
Drug discovery aims at finding new compounds with specific chemical properties for the
treatment of diseases. In the last years, the approach used in this search presents an …
treatment of diseases. In the last years, the approach used in this search presents an …
Deep learning improves prediction of drug–drug and drug–food interactions
Drug interactions, including drug–drug interactions (DDIs) and drug–food constituent
interactions (DFIs), can trigger unexpected pharmacological effects, including adverse drug …
interactions (DFIs), can trigger unexpected pharmacological effects, including adverse drug …
Artificial intelligence for drug toxicity and safety
Interventional pharmacology is one of medicine's most potent weapons against disease.
These drugs, however, can result in damaging side effects and must be closely monitored …
These drugs, however, can result in damaging side effects and must be closely monitored …
[HTML][HTML] BreastScreening-AI: Evaluating medical intelligent agents for human-AI interactions
In this paper, we developed BreastScreening-AI within two scenarios for the classification of
multimodal beast images:(1) Clinician-Only; and (2) Clinician-AI. The novelty relies on the …
multimodal beast images:(1) Clinician-Only; and (2) Clinician-AI. The novelty relies on the …
The application of machine learning methods for prediction of metal sorption onto biochars
The adsorption of six heavy metals (lead, cadmium, nickel, arsenic, copper, and zinc) on 44
biochars were modeled using artificial neural network (ANN) and random forest (RF) based …
biochars were modeled using artificial neural network (ANN) and random forest (RF) based …
Let's talk about sex: Differences in drug therapy in males and females
Abstract Professor Henry Higgins in My Fair Lady said,'Why can'ta woman be more like a
man?'Perhaps unintended, such narration extends to the reality of current drug …
man?'Perhaps unintended, such narration extends to the reality of current drug …
Introduction of human-centric AI assistant to aid radiologists for multimodal breast image classification
In this research, we take an HCI perspective on the opportunities provided by AI techniques
in medical imaging, focusing on workflow efficiency and quality, preventing errors and …
in medical imaging, focusing on workflow efficiency and quality, preventing errors and …
Machine learning in pharmacometrics: Opportunities and challenges
M McComb, R Bies… - British Journal of Clinical …, 2022 - Wiley Online Library
The explosive growth in medical devices, imaging and diagnostics, computing, and
communication and information technologies in drug development and healthcare has …
communication and information technologies in drug development and healthcare has …
SFLLN: a sparse feature learning ensemble method with linear neighborhood regularization for predicting drug–drug interactions
Drug–drug interactions are one of the major concerns of drug discovery, and the accurate
prediction of drug–drug interactions is important for drug safety surveillance. However, most …
prediction of drug–drug interactions is important for drug safety surveillance. However, most …