prioritize-drug-targets-ls

Prioritize Drug Targets from Compound Structures

Purpose

Starting from one or more small-molecule structures, generate evidence-backed target hypotheses, assess compound quality and chemical-series structure, retrieve annotated neighbors and activity data, validate target/disease context, identify patent review questions, and propose orthogonal experiments and medicinal-chemistry directions.

This workflow supports hypothesis generation and prioritization. It does not prove target engagement, efficacy, safety, patent novelty, freedom to operate, or clinical value.

Use this skill when

• a user has a compound library and wants reverse target identification;
• a user asks what target a SMILES may modulate;
• a chemistry team wants series-level target hypotheses and SAR;
• target hypotheses need biological, pipeline, patent, and experimental validation;
• an evidence-backed shortlist is needed for biochemical, biophysical, cellular, genetic, or omics follow-up.

Do not use it for:

• forward design against a known target without a reverse-identification question;
• a patentability, novelty, FTO, infringement, or validity opinion;
• a competitive landscape without compound-led target hypotheses;
• biological target profiling where no compound evidence is involved.

Scope and intake

Required

• one or more SMILES, SDF/MOL records, or an approved structured compound file;
• stable compound IDs;
• intended decision: triage, target discovery, repurposing, off-target investigation, or series selection.

Strongly recommended

• stereochemistry, protonation/salt/tautomer convention and structure provenance;
• known activity, assay format, concentration, phenotype, or negative results;
• intended disease/tissue and route;
• library size and chemical-series relationships;
• known liabilities, PAINS/aggregator/fluorescence/redox concerns;
• comparator compounds and suspected target classes;
• countries/commercial timing if patent questions are requested;
• confidentiality and authorization for external structure services.

If the compound identity or stereochemistry is ambiguous, create separate structure versions; do not silently select one.

Capability and MCP gate

Use supplied authoritative chemistry/assay exports where available.

Verified global PatSnap MCPs that can support parts of the workflow:

The global marketplace page currently named chemistry-small-molecule reports zero callable tools. Therefore this skill does not claim an operational PatSnap MCP for ADMET prediction, MCS/scaffold analysis, structure/similarity search, patent-structure retrieval, or automated SAR extraction. Use those capabilities only when an authorized service is actually exposed, and record provider/tool/version/parameters. Otherwise request user exports or produce a protocol with an explicit coverage gap.

Evidence hierarchy

1. direct target-engagement and binding data with appropriate controls;
2. biochemical functional assays;
3. cellular target-dependent pharmacology and rescue/competition;
4. genetic perturbation concordance and resistance mutations;
5. chemoproteomics/thermal/proximity/affinity capture and orthogonal validation;
6. curated compound–target activity with exact structure and assay metadata;
7. high-quality structural neighbors and SAR;
8. phenotypic/pathway/omics signatures;
9. in silico similarity, pharmacophore, docking, or prediction;
10. scaffold analogy or target-class stereotype.

Lower levels generate hypotheses but cannot replace upper-level validation.

Six-step workflow

Step 0 — Standardize compounds and define the decision

Before the source's six analytical steps:

• assign stable compound IDs and versions;
• parse/validate molecular graph with a chemistry toolkit where available;
• preserve supplied and standardized structures;
• normalize salts/solvates only under documented rules;
• preserve stereochemistry and isotopes;
• enumerate only justified protonation/tautomer states;
• calculate canonical representations and checksums;
• detect duplicates and mixtures;
• record invalid/ambiguous structures;
• define assay/phenotype/disease context and confidentiality.

SMILES parenthesis balance alone is not sufficient validation.

Output: compound registry and standardization log.

Step 1 — Physicochemical, developability, and assay-interference profile

Purpose

Assess suitability for the intended assay and route, identify liabilities and interpretation risks, and prioritize—not automatically discard—compounds.

Properties

When a validated tool/export is available, capture:

• molecular weight, cLogP/logD and pKa context;
• H-bond donors/acceptors, TPSA, rotatable bonds, rings/aromaticity;
• solubility and permeability with method/model/domain;
• microsomal/hepatocyte stability, clearance, plasma protein binding;
• CYP/transporter interaction;
• hERG/ion-channel and broader safety signals;
• BBB/CNS exposure prediction when relevant;
• reactive/toxicophores, aggregation, PAINS-like and assay-interference alerts;
• prediction uncertainty and applicability domain.

The Rule of Five is a heuristic for some oral small molecules, not a universal drug-likeness law. Values outside MW 500/logP 0–5/HBD 5/HBA 10 do not automatically fail a molecule. ADMET predictions are model outputs, not measured facts.

Decision

Use categories:

• suitable for current assay;
• proceed with controls;
• deprioritize for current objective;
• reformulate/retest;
• data/model out of domain;
• invalid/insufficient structure.

Low predicted BBB does not eliminate CNS target biology; it indicates exposure/delivery questions. Predicted hERG risk does not require avoiding cardiovascular indications; it triggers orthogonal liability testing and chemistry review.

Output: compound quality/developability matrix with source/model/uncertainty.

Step 2 — Series, scaffold, and pharmacophore analysis

Operation

• cluster compounds with documented fingerprint, similarity and clustering method;
• identify Bemis–Murcko or other defined scaffolds;
• run MCS only within sensible series and record atom/bond settings, timeout and coverage;
• identify substituent vectors, matched molecular pairs and property/activity cliffs where data exists;
• distinguish frequent fragments from privileged pharmacophores;
• select representative compounds across clusters and outliers.

Interpretation

Scaffolds such as aminopyrimidine, quinazoline, imidazopyrimidine, indole, piperazine or piperidine can occur in many target classes. They may suggest search concepts but do not identify a target.

Output: series/scaffold clusters, representatives, diversity metrics, and preliminary target-class hypotheses with low evidence grade.

Step 3 — Structure and bioactivity retrieval; hypothesis generation

Retrieval routes

Use multiple routes where available:

• exact structure;
• stereochemistry-aware exact structure;
• substructure;
• multiple fingerprints/similarity metrics;
• pharmacophore/shape/3D similarity;
• scaffold and matched-pair search;
• curated bioactivity and patent structure tables;
• phenotype/signature similarity if supplied.

Record:

• standardized query structure/version;
• database and cutoff;
• fingerprint/descriptor and Tanimoto or other metric;
• threshold and maximum results;
• stereochemistry/salt/tautomer handling;
• assay organism, construct, format, endpoint, unit, relation and confidence;
• target identity and family;
• exact source/publication/patent links.

Tanimoto values are fingerprint-dependent. A threshold of 0.4, 0.7 or 0.8 has no universal biological or legal meaning. Run sensitivity checks and inspect neighbor quality.

Aggregate by evidence, not frequency alone

For each target hypothesis, summarize:

• number of independent chemotypes and sources;
• exact/high-quality annotated neighbors;
• quantitative activities and assay comparability;
• selectivity/counter-screen data;
• SAR coherence across the query series;
• structural/biophysical evidence;
• phenotype/pathway concordance;
• negative/conflicting evidence;
• database bias and promiscuity;
• confidence and key experiment.

Do not output a fixed Top 3 solely from target frequency. Return as many hypotheses as evidence supports and an unresolved group when evidence is weak.

Output: target hypothesis evidence table.

Step 4 — Target, disease, pipeline, and competitive validation

For each hypothesis:

Target identity

• normalize gene/protein/complex/isoform/species;
• distinguish direct target, pathway node, biomarker and phenotypic association;
• document binding site, mechanism and modality where known.

Biological evidence

• human genetics and causal/association strength;
• disease-expression and functional perturbation evidence;
• genetic/pharmacological concordance;
• tissue/cell context;
• safety and essentiality concerns;
• resistance or escape mechanisms;
• contradictory/null evidence.

Tractability and pipeline

• target class and pocket/structural evidence;
• approved drugs and current assets, with current source/date;
• clinical/preclinical status and attrition;
• modality and binding-site differentiation;
• known selectivity/safety issues;
• competitor density and whitespace under an explicit definition.

Clinical/commercial context

• indication and unmet need;
• addressable population and epidemiology with geography/year;
• standard of care and benchmark;
• biomarker/diagnostic requirements;
• market figures only with authoritative source, date, currency and methodology.

Avoid “red ocean/blue ocean” labels without a defined competitive measure. Approved drugs validate tractability but may reduce or increase strategic attractiveness depending on differentiation.

Output: target evidence cards and a prioritized validation sequence.

Step 5 — Patent landscape and FTO question screen

This step identifies patent questions; it is not a novelty or FTO opinion.

Search

• exact/substructure/similarity chemical searches when an actual structure service exists;
• name/identifier/Markush/scaffold/substituent and target/use/process/formulation text searches;
• applicant/inventor/classification/family expansion;
• claims and current claim versions;
• target jurisdictions and planned making/use/sale/import acts;
• official-register status/term verification for material candidates.

Claim mapping

An exact structure disclosed in a patent does not prove that a live claim covers it. A similar structure and high Tanimoto do not prove equivalents. For each material member:

• identify controlling claim/version;
• segment every limitation;
• map candidate structure, stereochemistry, salt, substituent, Markush definitions, use, dose, process and formulation;
• record mapped/not mapped/uncertain/missing evidence;
• verify jurisdiction, status, term and planned act;
• state questions for qualified counsel.

Priority categories

• critical claim review;
• high review;
• monitor pending/uncertain;
• low current relevance under stated assumptions;
• resolved for defined scenario based on verified counsel/license/status evidence.

Do not label “no hits” or “expired patent” as low FTO risk without search-coverage and family/status analysis. Do not route novelty analysis as a proxy for FTO.

Output: patent evidence map, claim-review shortlist, and counsel questions.

Step 6 — SAR synthesis and medicinal-chemistry hypotheses

Source data

Use patent examples, papers, databases, or user assays only with:

• exact compound structure and source;
• target/assay construct and species;
• assay type, endpoint, unit, relation (=, <, >, range);
• qualitative bins and cutoffs;
• replicate/statistical information where reported;
• source locator and retrieval date.

Do not combine IC50, Ki, Kd, EC50, percent inhibition, cellular potency and phenotypic endpoints as if directly comparable.

Analysis

• normalize structures and assays without altering original values;
• stratify by comparable assay groups;
• map substituent positions and matched pairs;
• identify potency/selectivity/property cliffs;
• separate correlation from causal structural interpretation;
• account for stereochemistry, tautomer, permeability, solubility and assay artifacts;
• identify sparse/confounded regions;
• propose multiple hypotheses and experiments.

Modification paths

For each proposed path:

• target/selectivity objective;
• structural change and rationale;
• supporting SAR/evidence;
• predicted property/liability impact with uncertainty;
• resistance or alternate-site rationale if relevant;
• synthetic feasibility;
• experiment and success/failure criteria;
• patentability/FTO search required.

Output: SAR table and prioritized experiment/chemistry paths—not guaranteed design solutions.

Hypothesis prioritization

Use an explicit multi-criteria decision table, without hiding uncertainty in a single score:

Show ratings, rationale, source IDs, confidence and sensitivity to assumptions. A target can rank highly for testing while remaining commercially unattractive or patent-constrained.

Experimental validation ladder

For priority hypotheses propose:

1. biochemical binding/activity with concentration response and controls;
2. orthogonal biophysical engagement (for example SPR, ITC, DSF, MST as appropriate);
3. cellular target engagement;
4. pathway/functional readout;
5. genetic knockout/knockdown/rescue or resistance mutation;
6. selectivity/counter-screen panel;
7. chemoproteomics/omics or unbiased deconvolution where needed;
8. exposure, permeability, solubility, stability and artifact controls;
9. disease-relevant model.

Define success, failure and interpretation for each experiment.

Outputs

Core report

1. scope, compound registry and data quality;
2. physicochemical/developability and assay-interference profile;
3. scaffold/series diversity;
4. target hypothesis evidence table;
5. target/disease/pipeline/competitive validation;
6. patent landscape and claim-review questions;
7. SAR and medicinal-chemistry hypotheses;
8. prioritized experiments, owners and timing;
9. sources, methods, uncertainty and limitations.

Optional presentation/PDF

Create PPTX or PDF only when requested and when the relevant artifact tools are available. Use a restrained scientific format and preserve citations and methods in every format.

Failure paths

• Invalid/ambiguous structure: quarantine and request correction; no prediction.
• No authorized chemistry service: produce a protocol/import schema and run only supported context/patent modules.
• Out-of-domain ADMET model: label unusable; do not score as failure.
• No credible annotated neighbors: expand orthogonal routes and recommend phenotypic/chemoproteomic deconvolution.
• Promiscuous/interfering compound: require counter-screens and artifact controls.
• Target identity conflict: retain alternatives and design discriminating experiments.
• Patent claim/status missing: keep as unresolved; do not infer clearance.
• Asynchronous external task: follow exposed tool polling guidance; do not hard-code a 10–30 second interval without the service contract.

Quality gates

• Structures are valid, versioned, standardized and confidentiality-authorized.
• Prediction model/version/domain and measured versus predicted values are explicit.
• Rule-of-Five/BBB/hERG are not used as universal automatic exclusions.
• Scaffold stereotypes are labeled low-level hypotheses.
• Similarity metrics/fingerprints/thresholds and sensitivity are documented.
• Target hypotheses aggregate independent quantitative evidence and counterevidence, not frequency alone.
• Target/disease/pipeline facts are current and source-backed.
• Patent disclosure, claims, status, family and FTO questions are separated.
• SAR comparisons use compatible assays or visible caveats.
• Every recommendation includes evidence, uncertainty and validation.
• All factual statements use stable source IDs.
• Every referenced service, patent, assay and result is verified and available or explicitly marked unavailable.

Legal and scientific boundary

Review patentability/FTO/infringement questions with qualified counsel. Confirm target engagement, efficacy, safety and developability experimentally. Treat all computational outputs as hypotheses within their applicability domains.