SLWise Data

Data for SLWise model training/evaluation</p

Modified Electrostatic Complementary Score Function and Its Application Boundary Exploration in Drug Design

In recent years, machine learning (ML) models have been found to quickly predict various molecular properties with accuracy comparable to high-level quantum chemistry methods. One such example is the calculation of electrostatic potential (ESP). Different ESP prediction ML models were proposed to generate surface molecular charge distribution. Electrostatic complementarity (EC) can apply ESP data to quantify the complementarity between a ligand and its binding pocket, leading to the potential to increase the efficiency of drug design. However, there is not much research discussing EC score functions and their applicability domain. We propose a new EC score function modified from the one originally developed by Bauer and Mackey, and confirm its effectiveness against the available Pearson’s R correlation coefficient. Additionally, the applicability domain of the EC score and two indices used to define the EC score application scope will be discussed

Modified Electrostatic Complementary Score Function and Its Application Boundary Exploration in Drug Design

In recent years, machine learning (ML) models have been found to quickly predict various molecular properties with accuracy comparable to high-level quantum chemistry methods. One such example is the calculation of electrostatic potential (ESP). Different ESP prediction ML models were proposed to generate surface molecular charge distribution. Electrostatic complementarity (EC) can apply ESP data to quantify the complementarity between a ligand and its binding pocket, leading to the potential to increase the efficiency of drug design. However, there is not much research discussing EC score functions and their applicability domain. We propose a new EC score function modified from the one originally developed by Bauer and Mackey, and confirm its effectiveness against the available Pearson’s R correlation coefficient. Additionally, the applicability domain of the EC score and two indices used to define the EC score application scope will be discussed

Identifying New Ligands for JNK3 by Fluorescence Thermal Shift Assays and Native Mass Spectrometry

The c-Jun N-terminal kinases (JNKs) are evolutionary highly conserved serine/threonine kinases. Numerous findings suggest that JNK3 is involved in the pathogenesis of neurodegenerative diseases, so the inhibition of JNK3 may be a potential therapeutic intervention. The identification of novel compounds with promising pharmacological properties still represents a challenge. Fluorescence thermal shift screening of a chemically diversified lead-like scaffold library of 2024 pure compounds led to the initial identification of seven JNK3 binding hits, which were classified into four scaffold groups according to their chemical structures. Native mass spectrometry validated the interaction of 4 out of the 7 hits with JNK3. Binding geometries and interactions of the top 2 hits were evaluated by docking into a JNK3 crystal structure. Hit 5 had a Kd of 21 μM with JNK3 suggested scaffold 5-(phenylamino)-1H-1,2,3-triazole-4-carboxamide as a novel and selective JNK3 binder