Generative neural networks trained on SMILES can design innovative bioactive molecules . These so-called chemical language models (CLMs) have typically been trained on tens of template molecules for fine-tuning. However, it is challenging to apply CL...
With the wide application of deep learning in Drug Discovery, deep generative model has shown its advantages in drug molecular generation. Generative adversarial networks can be used to learn the internal structure of molecules, but the training proc...
We present the development and demonstration of a neural network (NN) model for fast and accurate prediction of whether or not a chosen analyte is focused by an isotachophoresis (ITP) buffer system. The NN model is useful in the rapid evaluation of p...
Journal of environmental science and health. Part A, Toxic/hazardous substances & environmental engineering
38613163
Heavy metal ions are considered to be the most prevalent and toxic water contaminants. The objective of thois work was to investigate the effectiveness of employing the adsorption technique in a laboratory-size reactor to remove copper (II) ions from...
Journal of chemical information and modeling
38346241
There has been a growing recognition of the need for diversity and inclusion in scientific fields. This trend is reflected in the Journal of Chemical Information and Modeling (JCIM), where there has been a gradual increase in the number of papers tha...
During gasification the kinetic and thermodynamic parameter depend on both the feedstock and the process conditions. As a result, one needs to enhance the understanding of how to model numerically these parameters using thermogravimetric analyzer. Co...
Journal of chemical information and modeling
38489239
Chemical reactions serve as foundational building blocks for organic chemistry and drug design. In the era of large AI models, data-driven approaches have emerged to innovate the design of novel reactions, optimize existing ones for higher yields, an...
In this study, we focus on the development of Quantitative Structure-Property Relationship (QSPR) models to predict the critical micelle concentration (CMC) for per- and polyfluoroalkyl substances (PFASs). Experimental CMC values for both fluorinated...
The prediction of thermodynamic properties of carbon-based molecules based on their geometrical conformation using fluctuation and density functional theories has achieved great success in the field of energy chemistry, while the excessive computatio...
The accurate prediction of standard vaporization enthalpy (ΔH°) for volatile organic compounds (VOCs) is of paramount importance in environmental chemistry, industrial applications and regulatory compliance. To overcome traditional experimental metho...