Virtual Screening
Accelerate hit discovery by screening thousands to millions of compounds through high-throughput virtual screening, molecular docking, and structure-based computational analysis.
Overview
Virtual Screening is a computational approach for rapidly identifying promising compounds from large chemical libraries based on their predicted interactions, physicochemical properties, and biological or functional potential. At TSLab, we provide end-to-end virtual screening services that integrate high-throughput molecular docking, ligand- and structure-based screening strategies, artificial intelligence (AI), and advanced computational analyses to accelerate hit identification and lead discovery.
Our workflows are designed to efficiently screen thousands to millions of compounds from public or proprietary chemical libraries while maintaining scientific accuracy and computational efficiency. Supported by high-performance computing (HPC) infrastructure, we deliver reliable candidate prioritization for applications in drug discovery, materials science, catalysis, and molecular engineering.
Applications
Our virtual screening services support a broad range of research and industrial applications, including:
- Drug discovery and lead identification
- Natural product screening
- Structure-based and ligand-based virtual screening
- Fragment-based screening
- Materials and functional molecule discovery
- Catalyst and ligand design
- Chemical library prioritization
- Hit-to-lead optimization
Capabilities
We provide comprehensive virtual screening workflows, including:
- Chemical Library Preparation – Standardization, structure cleaning, protonation, stereochemistry verification, and compound filtering.
- Drug-Likeness & Property Filtering – Lipinski, Veber, Ghose, PAINS, Brenk, REOS, and customized filtering strategies.
- High-Throughput Virtual Screening (HTVS) – Efficient screening of thousands to millions of compounds using optimized computational workflows.
- Molecular Docking – Single-target, multi-target, ensemble, induced-fit, and customized docking protocols.
- AI-Assisted Candidate Prioritization – Machine learning-assisted compound ranking and intelligent hit selection.
- Binding Interaction Analysis – Hydrogen bonds, hydrophobic contacts, electrostatic interactions, metal coordination, and interaction fingerprint analysis.
- Binding Affinity Prediction – Consensus scoring, rescoring, MM/GBSA, MM/PBSA, and advanced free-energy evaluation.
- ADMET Prediction – Evaluation of absorption, distribution, metabolism, excretion, toxicity, and physicochemical properties.
- Chemical Diversity Analysis – Clustering, scaffold analysis, similarity searching, and diversity assessment.
- Hit Prioritization – Multi-parameter optimization to identify the most promising candidates for experimental validation.
Contact
Jl. Tamalate 3 no. 136 Makassaar
contact@tslab.id
6282-33333-1270