Artificial intelligence to deep learning: machine intelligence approach for drug discovery

R Gupta, D Srivastava, M Sahu, S Tiwari, RK Ambasta… - Molecular …, 2021 - Springer
Drug designing and development is an important area of research for pharmaceutical
companies and chemical scientists. However, low efficacy, off-target delivery, time …

Current status and future directions for a neurotoxicity hazard assessment framework that integrates in silico approaches

KM Crofton, A Bassan, M Behl, YG Chushak… - Computational …, 2022 - Elsevier
Neurotoxicology is the study of adverse effects on the structure or function of the developing
or mature adult nervous system following exposure to chemical, biological, or physical …

Predicting potential drug-drug interactions on topological and semantic similarity features using statistical learning

A Kastrin, P Ferk, B Leskošek - PloS one, 2018 - journals.plos.org
Drug-drug interaction (DDI) is a change in the effect of a drug when patient takes another
drug. Characterizing DDIs is extremely important to avoid potential adverse drug reactions …

Artificial Intelligence and Machine learning based prediction of resistant and susceptible mutations in Mycobacterium tuberculosis

S Jamal, M Khubaib, R Gangwar, S Grover, A Grover… - Scientific reports, 2020 - nature.com
Tuberculosis (TB), an infectious disease caused by Mycobacterium tuberculosis (M. tb),
causes highest number of deaths globally for any bacterial disease necessitating novel …

Prediction of adverse drug reactions based on knowledge graph embedding

F Zhang, B Sun, X Diao, W Zhao, T Shu - BMC Medical Informatics and …, 2021 - Springer
Abstract Background Adverse drug reactions (ADRs) are an important concern in the
medication process and can pose a substantial economic burden for patients and hospitals …

Prediction of drug side effects with a refined negative sample selection strategy

H Liang, L Chen, X Zhao… - … and Mathematical Methods …, 2020 - Wiley Online Library
Drugs are an important way to treat various diseases. However, they inevitably produce side
effects, bringing great risks to human bodies and pharmaceutical companies. How to predict …

[HTML][HTML] A knowledge graph embedding based approach to predict the adverse drug reactions using a deep neural network

P Joshi, V Masilamani, A Mukherjee - Journal of Biomedical Informatics, 2022 - Elsevier
Abstract Recently Artificial Intelligence (AI) has not only been used to diagnose the disease
but also to cure the disease. Researchers started using AI for drug discovery. Predicting the …

Similarity‐Based Method with Multiple‐Feature Sampling for Predicting Drug Side Effects

Z Wu, L Chen - Computational and mathematical methods in …, 2022 - Wiley Online Library
Drugs can treat different diseases but also bring side effects. Undetected and unaccepted
side effects for approved drugs can greatly harm the human body and bring huge risks for …

An extensive survey on the use of supervised machine learning techniques in the past two decades for prediction of drug side effects

P Das, DH Mazumder - Artificial Intelligence Review, 2023 - Springer
Approved drugs for sale must be effective and safe, implying that the drug's advantages
outweigh its known harmful side effects. Side effects (SE) of drugs are one of the common …

The normative challenges of AI in outer space: law, ethics, and the realignment of terrestrial standards

U Pagallo, E Bassi, M Durante - Philosophy & Technology, 2023 - Springer
The paper examines the open problems that experts of space law shall increasingly address
over the next few years, according to four different sets of legal issues. Such differentiation …