[HTML][HTML] Drug discovery with explainable artificial intelligence
Deep learning bears promise for drug discovery, including advanced image analysis,
prediction of molecular structure and function, and automated generation of innovative …
prediction of molecular structure and function, and automated generation of innovative …
Explainable Artificial Intelligence (XAI): Concepts, taxonomies, opportunities and challenges toward responsible AI
In the last few years, Artificial Intelligence (AI) has achieved a notable momentum that, if
harnessed appropriately, may deliver the best of expectations over many application sectors …
harnessed appropriately, may deliver the best of expectations over many application sectors …
[HTML][HTML] DeepTox: toxicity prediction using deep learning
The Tox21 Data Challenge has been the largest effort of the scientific community to compare
computational methods for toxicity prediction. This challenge comprised 12,000 …
computational methods for toxicity prediction. This challenge comprised 12,000 …
QSAR modeling: where have you been? Where are you going to?
A Cherkasov, EN Muratov, D Fourches… - Journal of medicinal …, 2014 - ACS Publications
Quantitative structure–activity relationship modeling is one of the major computational tools
employed in medicinal chemistry. However, throughout its entire history it has drawn both …
employed in medicinal chemistry. However, throughout its entire history it has drawn both …
[HTML][HTML] Deep-learning: investigating deep neural networks hyper-parameters and comparison of performance to shallow methods for modeling bioactivity data
Background In recent years, research in artificial neural networks has resurged, now under
the deep-learning umbrella, and grown extremely popular. Recently reported success of DL …
the deep-learning umbrella, and grown extremely popular. Recently reported success of DL …
Predicting academic performance by considering student heterogeneity
The capacity to predict student academic outcomes is of value for any educational institution
aiming to improve student performance and persistence. Based on the generated …
aiming to improve student performance and persistence. Based on the generated …
Explainable machine learning for property predictions in compound optimization: miniperspective
R Rodríguez-Pérez, J Bajorath - Journal of medicinal chemistry, 2021 - ACS Publications
The prediction of compound properties from chemical structure is a main task for machine
learning (ML) in medicinal chemistry. ML is often applied to large data sets in applications …
learning (ML) in medicinal chemistry. ML is often applied to large data sets in applications …
[HTML][HTML] Large-scale comparison of machine learning methods for drug target prediction on ChEMBL
Deep learning is currently the most successful machine learning technique in a wide range
of application areas and has recently been applied successfully in drug discovery research …
of application areas and has recently been applied successfully in drug discovery research …
Advancing computational toxicology by interpretable machine learning
Chemical toxicity evaluations for drugs, consumer products, and environmental chemicals
have a critical impact on human health. Traditional animal models to evaluate chemical …
have a critical impact on human health. Traditional animal models to evaluate chemical …
[HTML][HTML] eToxPred: a machine learning-based approach to estimate the toxicity of drug candidates
Background The efficiency of drug development defined as a number of successfully
launched new pharmaceuticals normalized by financial investments has significantly …
launched new pharmaceuticals normalized by financial investments has significantly …