Prediction of Activity and Selectivity Profiles of Sigma Receptor Ligands Using Machine Learning Approaches by Lisa Lombardo (7301969)

Subjects:

AutoDesigner - Core Design, a De Novo Design Algorithm for Chemical Scaffolds: Application to the Design and Synthesis of Novel Selective Wee1 Inhibitors by Pieter H. Bos (1878256)

Subjects:

Overview of the ligand data preprocessing. by Ubair Aziz (21485738)

Computational Pipeline for Accelerating the Design of Glycomimetics by Yao Xiao (227567)

“…Putative glycomimetics are assembled by grafting small drug-like moieties onto the native carbohydrate scaffold in the presence of the receptor protein, with the moieties chosen from a virtual library of more than 1500 molecular fragments, selected for their synthetic accessibility. The method is illustrated for the case of glycomimetics but is generalizable to any bound ligand. …”

An overview of PATH⁺. by Yuxi Long (11024307)

Construction of internuclear persistence contours (IPCs). by Yuxi Long (11024307)

CorrEA: A Web Server for Optimizing Correlations between Calculated Energies and Activities in Ligand–Receptor Systems Considering Multiple Binding Site Conformations by Sergio Alfaro (9765813)

“…CorrEA performs a genetic algorithm (GA) selection to extract a representative complex for each ligand that better adjusts the global correlation between calculated docking energy values and experimental logarithmic biological activities. …”

Identification and validation of hub DE-podos in bulk RNA-seq. by Hailin Li (453832)

Unveiling cellular heterogeneity through podocyte subcluster analysis. by Hailin Li (453832)

Workflow of the study. by Hailin Li (453832)

Data Sheet 1_PD-L1 expression predicts the efficacy of PD-1 blockade plus chemotherapy versus chemotherapy alone in treatment-naïve advanced or metastatic gastric cancer: a pooled... by Wei Zhou (24328)

“…A combined positive score (CPS) of 5 was selected as the cutoff for analysis, with CheckMate 649 and ORIENT-16 trials included. …”

Overview of the research process. by Pedro Fong (2378413)

“…We used the automated docking suite GOLD v5.5 with the genetic algorithm to simulate molecular docking and predict the protein-ligand binding modes, and the ChemPLP empirical scoring function to estimate the binding affinities of 2,115 FDA-approved drugs to the human Ca_v3.1 channel. …”

Machine learning-driven identification of shared hub genes. by Weiming Cai (135737)

Machine Learning-Driven Methods for Nanobody Affinity Prediction by Hua Feng (234718)

“…In the current study, 12 machine learning algorithms were compared in parallel to explore the potential patterns between Nb–ligand affinity and eight noncovalent interactions. …”

Image 1_Targeting a distinct binding pocket in the pregnane X receptor with natural agonist TRLW-2 ameliorates murine ulcerative colitis.tif by Shangrui Rao (18189241)

“…Background<p>The therapeutic development of pregnane X receptor (PXR) agonists for ulcerative colitis (UC) is hindered by the poor selectivity of the canonical ligand-binding pocket. …”

Image 2_Targeting a distinct binding pocket in the pregnane X receptor with natural agonist TRLW-2 ameliorates murine ulcerative colitis.tif by Shangrui Rao (18189241)

“…Background<p>The therapeutic development of pregnane X receptor (PXR) agonists for ulcerative colitis (UC) is hindered by the poor selectivity of the canonical ligand-binding pocket. …”

Image 3_Targeting a distinct binding pocket in the pregnane X receptor with natural agonist TRLW-2 ameliorates murine ulcerative colitis.tif by Shangrui Rao (18189241)

“…Background<p>The therapeutic development of pregnane X receptor (PXR) agonists for ulcerative colitis (UC) is hindered by the poor selectivity of the canonical ligand-binding pocket. …”

Image 4_Targeting a distinct binding pocket in the pregnane X receptor with natural agonist TRLW-2 ameliorates murine ulcerative colitis.tif by Shangrui Rao (18189241)

“…Background<p>The therapeutic development of pregnane X receptor (PXR) agonists for ulcerative colitis (UC) is hindered by the poor selectivity of the canonical ligand-binding pocket. …”

Table 1_Integrated transcriptomic and network analysis reveals candidate immune–metabolic biomarkers in children with the inattentive type of ADHD.xlsx by Qiaoyan Shao (22357861)

“…High-confidence biomarkers were selected via a multi-step pipeline combining protein-protein interaction (PPI) network analysis and machine learning feature selection (LASSO regression, Boruta algorithm). …”

Image 1_Integrated transcriptomic and network analysis reveals candidate immune–metabolic biomarkers in children with the inattentive type of ADHD.tif by Qiaoyan Shao (22357861)

“…High-confidence biomarkers were selected via a multi-step pipeline combining protein-protein interaction (PPI) network analysis and machine learning feature selection (LASSO regression, Boruta algorithm). …”