admet-prediction

Name: admet-prediction
Author: GPTomics

1.1kstars185forks

How to install admet-prediction?

gptomics/bioskills/admet-prediction

$npx -y skills add GPTomics/bioSkills --skill admet-prediction

Installs into the current project.

Summary

Predicts ADMET properties using ADMETlab 3.0 (119 platform features, including 77 prediction models with modeled-endpoint uncertainty), ADMET-AI, DeepChem MolNet, and chemprop D-MPNN with explicit handling of OECD QSAR principles, applicability domain assessment, calibration, hER

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SKILL.md

1	## Version Compatibility
2
3	Reference examples tested with: RDKit 2024.09+, requests 2.31+, DeepChem 2.8+, chemprop 2.0+ (note major API change from 1.x), admet-ai 1.3+, pandas 2.2+.
4
5	Before using code patterns, verify installed versions match. If versions differ:
6	- Python: `pip show <package>` then `help(module.function)` to check signatures
7
8	If code throws ImportError, AttributeError, or TypeError, introspect the installed
9	package and adapt the example to match the actual API rather than retrying.
10
11	# ADMET Prediction
12
13	Predict absorption, distribution, metabolism, excretion, and toxicity properties of drug candidates. ADMET prediction underpins lead selection and de-risking; calibrated, applicability-domain-aware predictions distinguish a working filter from a costly false-confidence rejection. Modern best practice combines online services (ADMETlab 3.0 with uncertainty estimates), open-source models (chemprop D-MPNN), and rule-based filters (Lipinski / Veber / BBB heuristics) -- each with known failure modes.
14
15	For PAINS / Brenk / structural alerts, see `chemoinformatics/substructure-search`. For QSAR model building from in-house data, see `chemoinformatics/qsar-modeling`.
16
17	## ADMET Model Taxonomy
18
19	\| Tool \| Endpoints \| Architecture \| Uncertainty \| Access \| Fails when \|
20	\|------\|-----------\|--------------\|-------------\|--------\|------------\|
21	\| ADMETlab 3.0 \| 119 reported features: 77 prediction models, 34 computed properties, 8 rules \| Multi-task DMPNN + descriptors for modeled endpoints \| Evidential uncertainty for modeled endpoints \| Web service; hosted API documented by the authors \| Outside training distribution; metals; macrocycles \|
22	\| ADMET-AI \| TDC-derived ADMET tasks; inspect installed model metadata \| Chemprop D-MPNN \| Inspect version-specific outputs; do not assume calibrated uncertainty \| Python package \| v2 package predictions differ from the v1 paper/server \|
23	\| DeepChem MolNet \| Dataset-dependent tasks including Tox21, ToxCast, and ClinTox \| Model-dependent \| Model-dependent \| Python package \| Coverage and uncertainty depend on the selected dataset/model \|
24	\| pkCSM \| Service-defined ADMET endpoints \| Graph signatures + ML \| Inspect current service output \| Web service \| Applicability domain and service contract must be checked \|
25	\| SwissADME \| Physchem, pharmacokinetics, drug-likeness, and medchem outputs \| Published models and rules \| None advertised \| Web service (no public API) \| Automated access is restricted by its terms \|
26	\| ProTox-3.0 \| 61 toxicity models/endpoints \| RF/DNN + fingerprints, similarity, and pharmacophore methods \| Confidence score \| Web service / sample API \| Toxicity only; reports LD50 and toxicity class \|
27	\| ADMETpredictor (Simulations Plus) \| ~140 \| Proprietary \| Per-prediction \| Commercial \| License cost \|
28	\| FAF-Drugs4 \| filters \| Rule-based \| None \| Web \| Static rules \|
29	\| chemprop (in-house) \| User-defined \| D-MPNN ± descriptors \| Ensemble and other estimators; optional calibration \| Python package \| Requires suitable training and calibration data \|
30
31	Decision: For batch screening with no in-house data, ADMETlab 3.0 provides 119 reported platform features and uncertainty for modeled endpoints through its web service and hosted API; verify the live API documentation before automating access. For a sufficiently large, relevant in-house endpoint dataset, benchmark a chemprop D-MPNN, descriptors, and simpler baselines under a deployment-relevant split rather than assuming a universal sample-size threshold. Shan et al. (2022) reported an AUC of 0.956 for a D-MPNN combined with 206 MOE descriptors on their random-split hERG benchmark.
32
33	## Decision Tree by Scenario
34
35	\| Scenario \| Workflow \| Reasoning \|
36	\|----------\|----------\|-----------\|
37	\| Library triage, no in-house data \| ADMETlab 3.0 API batch \| Broad platform coverage plus modeled-endpoint uncertainty \|
38	\| Single endpoint, adequate in-house data \| Benchmark chemprop D-MPNN, descriptors, and simpler baselines \| Select by prospective or deployment-relevant validation \|
39	\| Need calibrated probabilities \| chemprop with ensemble + Platt \| Native deep learning rarely calibrated \|
40	\| FDA / regulatory submission \| OECD-compliant QSAR with AD \| See OECD principles below \|
41	\| Quick annotation for VS \| Lipinski, Veber, and QED reported separately \| Rank or annotate; do not impose a universal QED gate \|
42	\| BBB penetration \| Simple screen: TPSA <= 90, MW <= 500,

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admet-prediction

1.1kstars185forks

View on GitHub

How to install admet-prediction?

gptomics/bioskills/admet-prediction

$npx -y skills add GPTomics/bioSkills --skill admet-prediction

Installs into the current project.

Summary

Files · 1

View on GitHub

SKILL.md

1	## Version Compatibility
2
3	Reference examples tested with: RDKit 2024.09+, requests 2.31+, DeepChem 2.8+, chemprop 2.0+ (note major API change from 1.x), admet-ai 1.3+, pandas 2.2+.
4
5	Before using code patterns, verify installed versions match. If versions differ:
6	- Python: `pip show <package>` then `help(module.function)` to check signatures
7
8	If code throws ImportError, AttributeError, or TypeError, introspect the installed
9	package and adapt the example to match the actual API rather than retrying.
10
11	# ADMET Prediction
12
13	Predict absorption, distribution, metabolism, excretion, and toxicity properties of drug candidates. ADMET prediction underpins lead selection and de-risking; calibrated, applicability-domain-aware predictions distinguish a working filter from a costly false-confidence rejection. Modern best practice combines online services (ADMETlab 3.0 with uncertainty estimates), open-source models (chemprop D-MPNN), and rule-based filters (Lipinski / Veber / BBB heuristics) -- each with known failure modes.
14
15	For PAINS / Brenk / structural alerts, see `chemoinformatics/substructure-search`. For QSAR model building from in-house data, see `chemoinformatics/qsar-modeling`.
16
17	## ADMET Model Taxonomy
18
19	\| Tool \| Endpoints \| Architecture \| Uncertainty \| Access \| Fails when \|
20	\|------\|-----------\|--------------\|-------------\|--------\|------------\|
21	\| ADMETlab 3.0 \| 119 reported features: 77 prediction models, 34 computed properties, 8 rules \| Multi-task DMPNN + descriptors for modeled endpoints \| Evidential uncertainty for modeled endpoints \| Web service; hosted API documented by the authors \| Outside training distribution; metals; macrocycles \|
22	\| ADMET-AI \| TDC-derived ADMET tasks; inspect installed model metadata \| Chemprop D-MPNN \| Inspect version-specific outputs; do not assume calibrated uncertainty \| Python package \| v2 package predictions differ from the v1 paper/server \|
23	\| DeepChem MolNet \| Dataset-dependent tasks including Tox21, ToxCast, and ClinTox \| Model-dependent \| Model-dependent \| Python package \| Coverage and uncertainty depend on the selected dataset/model \|
24	\| pkCSM \| Service-defined ADMET endpoints \| Graph signatures + ML \| Inspect current service output \| Web service \| Applicability domain and service contract must be checked \|
25	\| SwissADME \| Physchem, pharmacokinetics, drug-likeness, and medchem outputs \| Published models and rules \| None advertised \| Web service (no public API) \| Automated access is restricted by its terms \|
26	\| ProTox-3.0 \| 61 toxicity models/endpoints \| RF/DNN + fingerprints, similarity, and pharmacophore methods \| Confidence score \| Web service / sample API \| Toxicity only; reports LD50 and toxicity class \|
27	\| ADMETpredictor (Simulations Plus) \| ~140 \| Proprietary \| Per-prediction \| Commercial \| License cost \|
28	\| FAF-Drugs4 \| filters \| Rule-based \| None \| Web \| Static rules \|
29	\| chemprop (in-house) \| User-defined \| D-MPNN ± descriptors \| Ensemble and other estimators; optional calibration \| Python package \| Requires suitable training and calibration data \|
30
31	Decision: For batch screening with no in-house data, ADMETlab 3.0 provides 119 reported platform features and uncertainty for modeled endpoints through its web service and hosted API; verify the live API documentation before automating access. For a sufficiently large, relevant in-house endpoint dataset, benchmark a chemprop D-MPNN, descriptors, and simpler baselines under a deployment-relevant split rather than assuming a universal sample-size threshold. Shan et al. (2022) reported an AUC of 0.956 for a D-MPNN combined with 206 MOE descriptors on their random-split hERG benchmark.
32
33	## Decision Tree by Scenario
34
35	\| Scenario \| Workflow \| Reasoning \|
36	\|----------\|----------\|-----------\|
37	\| Library triage, no in-house data \| ADMETlab 3.0 API batch \| Broad platform coverage plus modeled-endpoint uncertainty \|
38	\| Single endpoint, adequate in-house data \| Benchmark chemprop D-MPNN, descriptors, and simpler baselines \| Select by prospective or deployment-relevant validation \|
39	\| Need calibrated probabilities \| chemprop with ensemble + Platt \| Native deep learning rarely calibrated \|
40	\| FDA / regulatory submission \| OECD-compliant QSAR with AD \| See OECD principles below \|
41	\| Quick annotation for VS \| Lipinski, Veber, and QED reported separately \| Rank or annotate; do not impose a universal QED gate \|
42	\| BBB penetration \| Simple screen: TPSA <= 90, MW <= 500,

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6 picks

Type