Chemists can be skeptical in using deep learning (DL) in decision making, due to the lack of
interpretability in “black-box” models. Explainable artificial intelligence (XAI) is a branch of …

Solubility prediction remains a critical challenge in drug development, synthetic route and
chemical process design, extraction and crystallisation. Here we report a successful …

In this work, a novel strategy that combined molecular thermodynamic and machine learning
was proposed to accurately predict the solubility of drugs in various solvents. The strategy …

Experimental solubility data of cloxacillin benzathine in pure solvents and binary solvent
mixtures from 278.15 to 313.15 K were measured using a multiple reactor setup. The …

Prediction of drug solubility is a crucial problem in pharmaceutical industries for both drug
delivery and discovery purposes. Several theoretical approaches have been proposed to …

Aqueous solubility is a very important physical property of small molecule drugs and drug
candidates but also one of the most difficult to predict accurately. Aqueous solubility plays a …

The perturbed chain-statistical associating fluid theory equation of state (PC-SAFT EoS) is
one of the state-of-the-art thermodynamic models used in the phase equilibrium calculation …

Experimental solubility data of β-octahydro-1, 3, 5, 7-tetranitro-1, 3, 5, 7-tetrazocine (β-HMX)
in three binary solvent mixtures of water+(acetic acid, acetone or DMSO) over the …

The solubility of seven pharmaceutical compounds (paracetamol, benzoic acid, 4-
aminobenzoic acid, salicylic acid, ibuprofen, naproxen and temazepam) in pure and mixed …

In this study, the applicability of the perturbed chain statistical associating fluid theory (PC-
SAFT) was evaluated for pharmaceutical compounds. For this purpose, the parameters of …