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In Silico Hit & Lead Discovery Package
Virtual screening, molecular docking, and MD/MM-GBSA analyses to identify and prioritize high-value hits, refine leads, and deliver mechanism-aware candidate proposals with clear, data-driven go/no-go decisions.
This package is designed for biotech and pharma teams who want to accelerate early discovery, reduce wet-lab burden, and focus resources on the most promising chemotypes. We combine physics-based modeling and statistically robust scoring to move from large virtual libraries or existing hits toward a small, rationally prioritized set of candidates.
What we do
Target assessment & project setup
Review structural/biological data, define binding site(s) and modality (orthosteric/allosteric).
Prepare and validate protein structures (protonation states, tautomers, missing loops, cofactors, waters).
Virtual library design & filtering
Use commercial or proprietary libraries, or client-provided compounds.
Apply property, ADME, and PAINS/reactivity filters to focus on realistic, developable matter.
High-throughput virtual screening & docking
Multi-stage docking (fast screening → refined poses) with consensus scoring.
Generation of binding hypotheses and interaction fingerprints for key chemotypes.
MD refinement & MM/GBSA rescoring
Short to intermediate molecular dynamics simulations on selected complexes.
MM/GBSA or related free energy protocols for more reliable ranking of top candidates.
Mechanism-aware hit & lead selection
Analysis of binding modes, stability, water networks, and key residue interactions.
Identification of structure–based optimization handles (H-bond extensions, π–π, halogen/CH-π, allosteric pockets, etc.).
Deliverables
Shortlist of prioritized hits/leads with clear ranking and rationale.
Annotated binding poses (structures, images) ready for medicinal chemistry review.
Mechanism-aware report summarizing key interactions, liabilities, and optimization strategies.
Optional: SAR/series expansion suggestions and design rules for the next synthesis round.
Key benefits
Concentrates synthesis and bioassay budgets on the most promising candidates.
Provides transparent, data-driven go/no-go criteria at each stage.
Integrates seamlessly with in vitro assays and medicinal chemistry workflows, supporting faster, more informed decisions in preclinical discovery.
Virtual screening, molecular docking, and MD/MM-GBSA analyses to identify and prioritize high-value hits, refine leads, and deliver mechanism-aware candidate proposals with clear, data-driven go/no-go decisions.
This package is designed for biotech and pharma teams who want to accelerate early discovery, reduce wet-lab burden, and focus resources on the most promising chemotypes. We combine physics-based modeling and statistically robust scoring to move from large virtual libraries or existing hits toward a small, rationally prioritized set of candidates.
What we do
Target assessment & project setup
Review structural/biological data, define binding site(s) and modality (orthosteric/allosteric).
Prepare and validate protein structures (protonation states, tautomers, missing loops, cofactors, waters).
Virtual library design & filtering
Use commercial or proprietary libraries, or client-provided compounds.
Apply property, ADME, and PAINS/reactivity filters to focus on realistic, developable matter.
High-throughput virtual screening & docking
Multi-stage docking (fast screening → refined poses) with consensus scoring.
Generation of binding hypotheses and interaction fingerprints for key chemotypes.
MD refinement & MM/GBSA rescoring
Short to intermediate molecular dynamics simulations on selected complexes.
MM/GBSA or related free energy protocols for more reliable ranking of top candidates.
Mechanism-aware hit & lead selection
Analysis of binding modes, stability, water networks, and key residue interactions.
Identification of structure–based optimization handles (H-bond extensions, π–π, halogen/CH-π, allosteric pockets, etc.).
Deliverables
Shortlist of prioritized hits/leads with clear ranking and rationale.
Annotated binding poses (structures, images) ready for medicinal chemistry review.
Mechanism-aware report summarizing key interactions, liabilities, and optimization strategies.
Optional: SAR/series expansion suggestions and design rules for the next synthesis round.
Key benefits
Concentrates synthesis and bioassay budgets on the most promising candidates.
Provides transparent, data-driven go/no-go criteria at each stage.
Integrates seamlessly with in vitro assays and medicinal chemistry workflows, supporting faster, more informed decisions in preclinical discovery.