Since the turn of the century, as millions of user's opinions are available on the web, sentiment analysis has become one of the most fruitful research fields in Natural Language Processing (NLP). Research on sentiment analysis has covered a wide ran...
Theoretical biology & medical modelling
Jul 20, 2020
BACKGROUND: Currently, due to the huge progress in the field of information technologies and computer equipment, it is important to use modern approaches of artificial intelligence in order to process extensive chemical information at creating new dr...
Over the past two decades, an in silico absorption, distribution, metabolism, and excretion (ADMET) platform has been created at Bayer Pharma with the goal to generate models for a variety of pharmacokinetic and physicochemical endpoints in early dru...
The last two decades saw the establishment of three-dimensional (3D) cell cultures as an acknowledged tool to investigate cell behaviour in a tissue-like environment. Cells growing in spheroids differentiate and develop different characteristics in c...
This review provides an overview of descriptions of atoms applied to the understanding of phenomena like chemical reactivity and selectivity, pK values, Site of Metabolism prediction, or hydrogen bond strengths, but also the substitution of quantum m...
There has been much recent interest in machine learning (ML) and molecular quantitative structure property relationships (QSPR). The present research evaluated modern ML-based methods implemented in commercial software (COSMOquick and Molecular Model...
The in vitro-in vivo extrapolation (IVIVE) approach for predicting total plasma clearance (CL) has been widely used to rank order compounds early in discovery. More recently, a computational machine learning approach utilizing physicochemical descrip...
We address the problem of determining from laboratory experiments the data necessary for a proper modeling of drug delivery and efficacy in anticancer therapy. There is an inherent difficulty in extracting the necessary parameters, because the experi...
Drug-drug interactions (DDIs) extraction is one of the important tasks in the field of biomedical relation extraction, which plays an important role in the field of pharmacovigilance. Previous neural network based models have achieved good performanc...
Journal of computer-aided molecular design
May 2, 2020
Difficulties in interpreting machine learning (ML) models and their predictions limit the practical applicability of and confidence in ML in pharmaceutical research. There is a need for agnostic approaches aiding in the interpretation of ML models re...