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LUCAI · PHARMA · TECHNOLOGY & SOFTWAREAN ATLAS OF PUBLISHED METHODS

THE TOOLKIT

Seven published
approaches to
molecular
discovery.

Seven published approaches address distinct questions in molecular discovery, from target prioritisation to knowledge-guided docking. Each stage links to its source.

APPROACHES
07
STAGES
04
PEER-REVIEWED PAPERS
07
PUBLISHED BETWEEN
2014 — 2022
EVERY PLATE THAT FOLLOWS CITES ITS SOURCE.01 — 04 ↓
STAGE 01 OF 04LUCAI · PHARMA

01WHICH TARGETS MERIT INVESTIGATION

Target & druggability

Target selection combines biological evidence with structural assessment. These resources help prioritise candidate pathogen proteins and inspect whether their pockets have features associated with druggability.

  1. METHOD 01

    Target-Pathogen

    Integrates essentiality, metabolic role, host similarity, structural druggability and other evidence to rank candidate pathogen targets.

    Sosa et al. Nucleic Acids Research 2018 · 46(D1):D413–D418
  2. METHOD 02

    TuberQ

    Catalogues predicted pocket druggability alongside structural and biological information for Mycobacterium tuberculosis proteins.

    Radusky et al. Database (Oxford) 2014 · bau035
STAGE 02 OF 04LUCAI · PHARMA

02WHAT INTERACTIONS THE SITE MAY SUPPORT

Interaction & solvent mapping

Solvent simulations can reveal interaction sites around a protein. Those sites provide hypotheses that can guide docking and virtual screening.

  1. METHOD 01

    WATCLUST

    Identifies and characterises water sites on a protein surface and transfers that information into docking.

    López et al. Bioinformatics 2015 · 31(22):3697–3699
  2. METHOD 02

    Cosolvent pharmacophore

    Derives interaction patterns from cosolvent simulations for conformational search and affinity scoring.

    Arcon et al. Journal of Chemical Information and Modeling 2019 · 59(8):3572–3583
STAGE 03 OF 04LUCAI · PHARMA

03TWO DIRECTIONS OF VIRTUAL SCREENING

Compounds & targets

Starting with a protein, LigQ selects compounds to screen. Starting with an active compound, reverse virtual screening prioritises possible target domains. These are separate workflows with different outputs.

STAGE 04 OF 04LUCAI · PHARMA

04WHERE THE SEARCH SHOULD LOOK

Knowledge-guided docking

Standard docking can incorporate prior interaction evidence. AutoDock Bias adds user-defined energy terms that favour selected interactions or ligand positions during the search.

  1. METHOD 01

    AutoDock Bias

    Adds user-defined biases to AutoDock4 energy maps to favour specified protein–ligand interactions or ligand positions.

    Arcon et al. Bioinformatics 2019 · 35(19):3836–3838
LUCAI · PHARMAEND OF ATLAS

Bring us the molecule that won’t behave.

These methods and strategies are described in peer-reviewed papers. Tell us where your workflow breaks, and we will assess which approaches fit the question.

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