Step-by-step guide to constructing an open-source drug discovery pipeline with AI and chemistry tools — from data to visualization.
A clear workflow for setting up, running, and analyzing a protein–ligand complex simulation using CHARMM36 and TIP3P in GROMACS.
Overview of key molecular simulation methods — QM, MD, CGMD, MC, BD, LD, DPD — and their strategic applications in drug discovery pipelines.
A step-by-step workflow to identify repurposing candidates by integrating heterogeneous biomedical networks, graph embeddings, and experimental prioritization.
Practical guide to checking AlphaFold-predicted protein models using pLDDT, PAE, geometry validation tools, structural comparisons, and functional tests.
Step-by-step tutorial on building an explainable ML model to predict blood-brain barrier permeability, using feature importance and SHAP analysis for interpretability.
Practical strategies and best practices for planning, writing, and publishing high-quality scientific research articles.
Comprehensive, metric-driven workflow for robust 3D-QSAR modeling, based on Xu et al., 2020 and enriched with cheminformatics best practices.
Step-by-step workflow and best practices for building a hybrid genome assembly pipeline using short- and long-read sequencing data.
Exploring how data science techniques—from machine learning to data visualization—are transforming chemistry and accelerating the discovery of new drugs and materials.
