In this work, for the first time, a deep eutectic solvent assisted ligand affinity assay was proposed. Several critical parameters affecting the analysis performance were investigated and the optimized DES extract conditions were as follows: the solu...
Journal of chemical information and modeling
40273444
The rapid expansion of readily accessible compounds over the past six years has transformed molecular docking, improving hit rates and affinities. While many millions of molecules may score well in a docking campaign, the results are rarely fully sha...
Journal of chemical information and modeling
40253627
Porphyrins are involved in numerous and very different chemical and biological processes, due to the sensitivity of their application-relevant properties to subtle structural changes. Applying modern machine learning methodology is very appealing for...
Riboswitches are a class of noncoding RNA structures that interact with target ligands to cause a conformational change that can then execute some regulatory purpose within the cell. Riboswitches are ubiquitous and well characterized in bacteria and ...
Methods in molecular biology (Clifton, N.J.)
40249485
Insects rely on olfaction in many aspects of their life, and odorant receptors are key proteins in this process. Whereas a plethora of insect odorant receptor sequences is available, most of them are still orphan or uncompletely characterized, since ...
Protein-ligand interactions are crucial in drug discovery. Accurately predicting protein-ligand binding affinity is essential for screening potential drugs. Graph neural networks have proven highly effective in modeling spatial relationships and thre...
Deep learning (DL) methods have drastically advanced structure-based drug discovery by directly predicting protein structures from sequences. Recently, these methods have become increasingly accurate in predicting complexes formed by multiple protein...
Journal of chemical information and modeling
40293047
This study introduces PROFIS, a new generative model capable of the design of structurally novel and target-focused compound libraries. The model relies on a recurrent neural network that was trained to decode embedded molecular fingerprints into SMI...
Traditional drug design faces significant challenges due to inherent chemical and biological complexities, often resulting in high failure rates in clinical trials. Deep learning advancements, particularly generative models, offer potential solutions...
Journal of chemical information and modeling
40241349
Artificial intelligence (AI) is revolutionizing drug discovery with unprecedented speed and efficiency. In computer-aided drug design, structure-based and ligand-based methodologies are the main driving forces for innovation. In cases where no experi...