Configuration

TOPBP (Complex)

TOPBP (Complex) is a complex-structure binding-affinity comparator listed in the DEELIG study.

Sources

DEELIG: A Deep Learning Approach to Predict Protein-Ligand Binding Affinity · Discussion (paragraph 7); Materials and Methods/Additional case studies of specific protein families (paragraph 3)

1 evaluation · 1 metric row

At a glance

Model type

Study-specific predictive method; this record is the paper-specific evaluated configuration.

Sources

DEELIG: A Deep Learning Approach to Predict Protein-Ligand Binding Affinity · Materials and Methods/Additional case studies of specific protein families (paragraph 2); Materials and Methods/Additional case studies of specific protein families (paragraph 3)

Biological inputs

Protein–ligand complex structure

Sources

DEELIG: A Deep Learning Approach to Predict Protein-Ligand Binding Affinity · Materials and Methods/Data set refinement (paragraph 3); Materials and Methods/Data set refinement (paragraph 1)

Outputs

Predicted binding affinity

Sources

DEELIG: A Deep Learning Approach to Predict Protein-Ligand Binding Affinity · Materials and Methods/Novel data set: raw data (paragraph 3); Materials and Methods/Novel data set: raw data (paragraph 2)

limited source coverage · Automated source review, 2026-09-16. All specifications and missing details

Evaluations and results

Release 2026-09-17-d277315f7d76 · 1 evaluation · 1 metric row. Different protocols are not a single leaderboard.

Evaluation setupMetricEvidence origin

Results grouped by the exact reported evaluation
Metric and finding	Coverage and uncertainty	Evidence
TOPBP (Complex): Protein–ligand binding affinity prediction Configuration: TOPBP (Complex)Task: Protein–ligand binding affinity predictionDataset: PDBbind core v2016 Source table compiles a previously published comparator; protocol equivalence is not established. Result quoted from another source · Evaluation metadata: needs review
0.861 Pearson R Unit: unitless · Direction: unknown	Uncertainty: not reported in legacy extract Scored: Not reported · Eligible: Not reported	source checkedDEELIG: A Deep Learning Approach to Predict Protein-Ligand Binding Affinity · Table 2, TOPBP (Complex) row, PDBbind v2016 column Source checking is not independent reproduction.

How it works

How the evaluated method works

This record preserves the complex-input comparator identity printed in the source table. The table does not establish which upstream TopBP implementation/checkpoint generated this exact row.

Sources

What was evaluated

The linked evaluation record identifies TOPBP (Complex): Protein–ligand binding affinity prediction. Its dataset, split, adaptation and evidence origin remain attached to the reported results.

Sources

DEELIG: A Deep Learning Approach to Predict Protein-Ligand Binding Affinity · The named evaluation’s methods and comparison table; exact preserved evaluation IDs: evaluation-lit-b3-043

Strengths and limitations

Strengths and considerations

Offers a complex-input reference against a method intended to work without a docked pose as user input.
Sources
DEELIG: A Deep Learning Approach to Predict Protein-Ligand Binding Affinity · Introduction (paragraph 3); Abstract (paragraph 1)

Limitations and conditions

The printed abbreviated identity has not been resolved to a unique upstream version; detailed architecture and licensing must not be inferred from the name alone.
Sources
DEELIG: A Deep Learning Approach to Predict Protein-Ligand Binding Affinity · Materials and Methods/Feature extraction/Ligand features (paragraph 3); Materials and Methods/Strategies (paragraph 1)

What remains unknown

An aggregate parameter total for this exact evaluated configuration is not established by the inspected sources.
TOPBP (Complex) is the comparison-table label; that label does not specify an immutable weight revision.
The Deelig comparison table identifies TOPBP (Complex) but does not resolve an exact implementation, fitted artifact or training configuration.
A maximum input/context length for this exact evaluated configuration is not established by the inspected sources.
No explicit code licence was established from the paper’s availability statement and inspected repository-root documentation.
The inspected model-access documentation does not explicitly identify terms for this exact evaluated checkpoint or fitted head; repository code terms are shown separately.
An immutable checkpoint or fitted-artifact digest is not supplied by the comparison-table identity.

Profile review details

Primary full text and the available official implementation/model documentation were inspected. Explanatory claims are source-backed; unresolved exact-configuration metadata is labelled explicitly. This is automated review, not a human review or independent benchmark reproduction.

Stable record: reported-model-0068c3eff1bf7b

Specifications

Inputs, training, access and other details

Explanatory profile: limited source coverage · Automated source review, 2026-09-16. Review applies to the cited claims; unresolved fields are listed below. Numerical results retain their own review status.

Inputs, outputs and configuration
Property	Description and evidence
Model type	Study-specific predictive method; this record is the paper-specific evaluated configuration. Sources DEELIG: A Deep Learning Approach to Predict Protein-Ligand Binding Affinity · Materials and Methods/Additional case studies of specific protein families (paragraph 2); Materials and Methods/Additional case studies of specific protein families (paragraph 3)
Architecture / procedure	This record preserves the complex-input comparator identity printed in the source table. The table does not establish which upstream TopBP implementation/checkpoint generated this exact row. Sources DEELIG: A Deep Learning Approach to Predict Protein-Ligand Binding Affinity · Materials and Methods/Additional case studies of specific protein families (paragraph 2); Materials and Methods/Additional case studies of specific protein families (paragraph 3)
Biological inputs	Protein–ligand complex structure Sources DEELIG: A Deep Learning Approach to Predict Protein-Ligand Binding Affinity · Materials and Methods/Data set refinement (paragraph 3); Materials and Methods/Data set refinement (paragraph 1)
Outputs	Predicted binding affinity Sources DEELIG: A Deep Learning Approach to Predict Protein-Ligand Binding Affinity · Materials and Methods/Novel data set: raw data (paragraph 3); Materials and Methods/Novel data set: raw data (paragraph 2)
Parameters	An aggregate parameter total for this exact evaluated configuration is not established by the inspected sources. · Not reported in inspected sources Sources (2) DEELIG: A Deep Learning Approach to Predict Protein-Ligand Binding Affinity; asadahmedtech/DEELIG README.md · Materials and Methods/Novel data set: raw data; Materials and Methods/Data set refinement; Materials and Methods/Feature extraction; Materials and Methods/Feature extraction/Protein-pocket features; Materials and Methods/Feature extraction/Ligand features; Materials and Methods/Feature extraction/Grid formation; Materials and Methods/Strategies; Materials and Methods/Strategies/Atomic model/Preprocessing; inspected for aggregate parameter count (component sizes are not added without an exact configuration); README.md at pinned repository revision
Known versions / configuration	TOPBP (Complex) is the comparison-table label; that label does not specify an immutable weight revision. · Not reported in inspected sources Sources DEELIG: A Deep Learning Approach to Predict Protein-Ligand Binding Affinity · Model identification in the comparison table and corresponding Methods; immutable checkpoint revision is not supplied by the table label.
Training data / fitting	The Deelig comparison table identifies TOPBP (Complex) but does not resolve an exact implementation, fitted artifact or training configuration. · Not reported in inspected sources Sources DEELIG: A Deep Learning Approach to Predict Protein-Ligand Binding Affinity · The Deelig model-comparison table; TOPBP (Complex) row
Context limits	A maximum input/context length for this exact evaluated configuration is not established by the inspected sources. · Not reported in inspected sources Sources (2) DEELIG: A Deep Learning Approach to Predict Protein-Ligand Binding Affinity; asadahmedtech/DEELIG README.md · Materials and Methods/Novel data set: raw data; Materials and Methods/Data set refinement; Materials and Methods/Feature extraction; Materials and Methods/Feature extraction/Protein-pocket features; Materials and Methods/Feature extraction/Ligand features; Materials and Methods/Feature extraction/Grid formation; Materials and Methods/Strategies; Materials and Methods/Strategies/Atomic model/Preprocessing; inspected for explicit maximum input length (dataset lengths and family-wide limits are not substituted); README.md at pinned repository revision
Access	Official study implementation and usage documentation: https://github.com/asadahmedtech/DEELIG/blob/3a3993fc903c40f1ce904111c8e085c79fb45df6/README.md. This pinned documentation revision is not automatically the evaluated weight revision. Sources asadahmedtech/DEELIG README.md · README.md; installation, model download and usage instructions
Code licence	No explicit code licence was established from the paper’s availability statement and inspected repository-root documentation. · Not reported in inspected sources Sources asadahmedtech/DEELIG README.md · README.md and repository-root licence-file search
Weights licence	The inspected model-access documentation does not explicitly identify terms for this exact evaluated checkpoint or fitted head; repository code terms are shown separately. · Not reported in inspected sources Sources asadahmedtech/DEELIG README.md · README.md; checkpoint/access documentation and licence scope

Evidence table

Inspect claims, sources and review details

Trace each statement to its source and review. A context-only reference supports the record generally; it does not verify an individual field. Source checking does not reproduce an experiment.

One row per statement and cited source. Multiple citations are not independent evaluations. Shared locators are labelled explicitly.

18 evidence rows matching the loaded filters

Claims, original sources and review scope · Release 2026-09-17-d277315f7d76
Property and statement	Original source and location	Review and provenance
Model type Study-specific predictive method; this record is the paper-specific evaluated configuration. Individual claims	DEELIG: A Deep Learning Approach to Predict Protein-Ligand Binding Affinity Original source ↗ Materials and Methods/Additional case studies of specific protein families (paragraph 2); Materials and Methods/Additional case studies of specific protein families (paragraph 3) Version: PMC archival version PMC8274096.1 Retrieved: 2026-09-16T10:33:55.586Z	source checked automated source review · 2026-09-16 Audit details Primary full text and the available official implementation/model documentation were inspected. Explanatory claims are source-backed; unresolved exact-configuration metadata is labelled explicitly. This is automated review, not a human review or independent benchmark reproduction. Field: `attributes.profile.facts.0.value` Source artifact SHA-256: `5a7620c18d0622561004e1e25b5cfaf7399e93df3547eeefdd4cf6d300bb8aba` Hash scope: Hash scope not separately documented; inspect source record Inspected artifact
Architecture / procedure This record preserves the complex-input comparator identity printed in the source table. The table does not establish which upstream TopBP implementation/checkpoint generated this exact row. Individual claims	DEELIG: A Deep Learning Approach to Predict Protein-Ligand Binding Affinity Original source ↗ Materials and Methods/Additional case studies of specific protein families (paragraph 2); Materials and Methods/Additional case studies of specific protein families (paragraph 3) Version: PMC archival version PMC8274096.1 Retrieved: 2026-09-16T10:33:55.586Z	source checked automated source review · 2026-09-16 Audit details Primary full text and the available official implementation/model documentation were inspected. Explanatory claims are source-backed; unresolved exact-configuration metadata is labelled explicitly. This is automated review, not a human review or independent benchmark reproduction. Field: `attributes.profile.facts.1.value` Source artifact SHA-256: `5a7620c18d0622561004e1e25b5cfaf7399e93df3547eeefdd4cf6d300bb8aba` Hash scope: Hash scope not separately documented; inspect source record Inspected artifact
Weights licence The inspected model-access documentation does not explicitly identify terms for this exact evaluated checkpoint or fitted head; repository code terms are shown separately. Individual claims	asadahmedtech/DEELIG README.md Original source ↗ README.md; checkpoint/access documentation and licence scope Version: 3a3993fc903c40f1ce904111c8e085c79fb45df6 Retrieved: 2026-09-16T19:54:13.759450+00:00	unreported automated source review · 2026-09-16 Audit details Primary full text and the available official implementation/model documentation were inspected. Explanatory claims are source-backed; unresolved exact-configuration metadata is labelled explicitly. This is automated review, not a human review or independent benchmark reproduction. Field: `attributes.profile.facts.10.value` Source artifact SHA-256: `a6c75971df142bd46ea22287f97aeafd861b035548cddef2bd8ad75f75f3aafd` Hash scope: Hash scope not separately documented; inspect source record Inspected artifact
Biological inputs Protein–ligand complex structure Individual claims	DEELIG: A Deep Learning Approach to Predict Protein-Ligand Binding Affinity Original source ↗ Materials and Methods/Data set refinement (paragraph 3); Materials and Methods/Data set refinement (paragraph 1) Version: PMC archival version PMC8274096.1 Retrieved: 2026-09-16T10:33:55.586Z	source checked automated source review · 2026-09-16 Audit details Primary full text and the available official implementation/model documentation were inspected. Explanatory claims are source-backed; unresolved exact-configuration metadata is labelled explicitly. This is automated review, not a human review or independent benchmark reproduction. Field: `attributes.profile.facts.2.value` Source artifact SHA-256: `5a7620c18d0622561004e1e25b5cfaf7399e93df3547eeefdd4cf6d300bb8aba` Hash scope: Hash scope not separately documented; inspect source record Inspected artifact
Outputs Predicted binding affinity Individual claims	DEELIG: A Deep Learning Approach to Predict Protein-Ligand Binding Affinity Original source ↗ Materials and Methods/Novel data set: raw data (paragraph 3); Materials and Methods/Novel data set: raw data (paragraph 2) Version: PMC archival version PMC8274096.1 Retrieved: 2026-09-16T10:33:55.586Z	source checked automated source review · 2026-09-16 Audit details Primary full text and the available official implementation/model documentation were inspected. Explanatory claims are source-backed; unresolved exact-configuration metadata is labelled explicitly. This is automated review, not a human review or independent benchmark reproduction. Field: `attributes.profile.facts.3.value` Source artifact SHA-256: `5a7620c18d0622561004e1e25b5cfaf7399e93df3547eeefdd4cf6d300bb8aba` Hash scope: Hash scope not separately documented; inspect source record Inspected artifact
Parameters An aggregate parameter total for this exact evaluated configuration is not established by the inspected sources. Individual claims	DEELIG: A Deep Learning Approach to Predict Protein-Ligand Binding Affinity Original source ↗ Materials and Methods/Novel data set: raw data; Materials and Methods/Data set refinement; Materials and Methods/Feature extraction; Materials and Methods/Feature extraction/Protein-pocket features; Materials and Methods/Feature extraction/Ligand features; Materials and Methods/Feature extraction/Grid formation; Materials and Methods/Strategies; Materials and Methods/Strategies/Atomic model/Preprocessing; inspected for aggregate parameter count (component sizes are not added without an exact configuration); README.md at pinned repository revision Shared locator for this statement’s cited sources; not a separate locator for each citation. Version: PMC archival version PMC8274096.1 Retrieved: 2026-09-16T10:33:55.586Z	unreported automated source review · 2026-09-16 Audit details Primary full text and the available official implementation/model documentation were inspected. Explanatory claims are source-backed; unresolved exact-configuration metadata is labelled explicitly. This is automated review, not a human review or independent benchmark reproduction. Field: `attributes.profile.facts.4.value` Source artifact SHA-256: `5a7620c18d0622561004e1e25b5cfaf7399e93df3547eeefdd4cf6d300bb8aba` Hash scope: Hash scope not separately documented; inspect source record Inspected artifact
Parameters An aggregate parameter total for this exact evaluated configuration is not established by the inspected sources. Individual claims	asadahmedtech/DEELIG README.md Original source ↗ Materials and Methods/Novel data set: raw data; Materials and Methods/Data set refinement; Materials and Methods/Feature extraction; Materials and Methods/Feature extraction/Protein-pocket features; Materials and Methods/Feature extraction/Ligand features; Materials and Methods/Feature extraction/Grid formation; Materials and Methods/Strategies; Materials and Methods/Strategies/Atomic model/Preprocessing; inspected for aggregate parameter count (component sizes are not added without an exact configuration); README.md at pinned repository revision Shared locator for this statement’s cited sources; not a separate locator for each citation. Version: 3a3993fc903c40f1ce904111c8e085c79fb45df6 Retrieved: 2026-09-16T19:54:13.759450+00:00	unreported automated source review · 2026-09-16 Audit details Primary full text and the available official implementation/model documentation were inspected. Explanatory claims are source-backed; unresolved exact-configuration metadata is labelled explicitly. This is automated review, not a human review or independent benchmark reproduction. Field: `attributes.profile.facts.4.value` Source artifact SHA-256: `a6c75971df142bd46ea22287f97aeafd861b035548cddef2bd8ad75f75f3aafd` Hash scope: Hash scope not separately documented; inspect source record Inspected artifact
Known versions / configuration TOPBP (Complex) is the comparison-table label; that label does not specify an immutable weight revision. Individual claims	DEELIG: A Deep Learning Approach to Predict Protein-Ligand Binding Affinity Original source ↗ Model identification in the comparison table and corresponding Methods; immutable checkpoint revision is not supplied by the table label. Version: PMC archival version PMC8274096.1 Retrieved: 2026-09-16T10:33:55.586Z	unreported automated source review · 2026-09-16 Audit details Primary full text and the available official implementation/model documentation were inspected. Explanatory claims are source-backed; unresolved exact-configuration metadata is labelled explicitly. This is automated review, not a human review or independent benchmark reproduction. Field: `attributes.profile.facts.5.value` Source artifact SHA-256: `5a7620c18d0622561004e1e25b5cfaf7399e93df3547eeefdd4cf6d300bb8aba` Hash scope: Hash scope not separately documented; inspect source record Inspected artifact
Training data / fitting The Deelig comparison table identifies TOPBP (Complex) but does not resolve an exact implementation, fitted artifact or training configuration. Individual claims	DEELIG: A Deep Learning Approach to Predict Protein-Ligand Binding Affinity Original source ↗ The Deelig model-comparison table; TOPBP (Complex) row Version: PMC archival version PMC8274096.1 Retrieved: 2026-09-16T10:33:55.586Z	unreported automated source review · 2026-09-16 Audit details Primary full text and the available official implementation/model documentation were inspected. Explanatory claims are source-backed; unresolved exact-configuration metadata is labelled explicitly. This is automated review, not a human review or independent benchmark reproduction. Field: `attributes.profile.facts.6.value` Source artifact SHA-256: `5a7620c18d0622561004e1e25b5cfaf7399e93df3547eeefdd4cf6d300bb8aba` Hash scope: Hash scope not separately documented; inspect source record Inspected artifact
Context limits A maximum input/context length for this exact evaluated configuration is not established by the inspected sources. Individual claims	DEELIG: A Deep Learning Approach to Predict Protein-Ligand Binding Affinity Original source ↗ Materials and Methods/Novel data set: raw data; Materials and Methods/Data set refinement; Materials and Methods/Feature extraction; Materials and Methods/Feature extraction/Protein-pocket features; Materials and Methods/Feature extraction/Ligand features; Materials and Methods/Feature extraction/Grid formation; Materials and Methods/Strategies; Materials and Methods/Strategies/Atomic model/Preprocessing; inspected for explicit maximum input length (dataset lengths and family-wide limits are not substituted); README.md at pinned repository revision Shared locator for this statement’s cited sources; not a separate locator for each citation. Version: PMC archival version PMC8274096.1 Retrieved: 2026-09-16T10:33:55.586Z	unreported automated source review · 2026-09-16 Audit details Primary full text and the available official implementation/model documentation were inspected. Explanatory claims are source-backed; unresolved exact-configuration metadata is labelled explicitly. This is automated review, not a human review or independent benchmark reproduction. Field: `attributes.profile.facts.7.value` Source artifact SHA-256: `5a7620c18d0622561004e1e25b5cfaf7399e93df3547eeefdd4cf6d300bb8aba` Hash scope: Hash scope not separately documented; inspect source record Inspected artifact

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Sources and history

Release 2026-09-17-d277315f7d76 · Record review: needs review

2 source records and release history

DEELIG: A Deep Learning Approach to Predict Protein-Ligand Binding Affinity · Original source · PMC archival version PMC8274096.1
asadahmedtech/DEELIG README.md · Original source · 3a3993fc903c40f1ce904111c8e085c79fb45df6

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Technical metadata and extraction receipts

Stable ID: reported-model-0068c3eff1bf7b

areas: molecular-interactions
entity level: method
version: Not reported
reported name: TOPBP (Complex)
historical missing metadata: version: not_reported_in_legacy_extract; checkpoint revision: not_reported_in_legacy_extract; training data: not_reported_in_legacy_extract; licence: not_reported_in_legacy_extract
metadata review scope: historical_missing_metadata preserves the original discovery state. Current descriptive evidence and missingness are recorded in profile.facts; numerical-result review is separate.
legacy kinds: model
entity classification: review date: 2026-09-17; rationale: This source-scoped entry preserves the method/configuration actually named in an evaluation. It is neither a global family identity nor proof of an immutable checkpoint; the linked evaluation retains adaptation, fitting and scoring details.; source ids: deelig-2021; source locator: Materials and Methods/Additional case studies of specific protein families (paragraph 2); Materials and Methods/Additional case studies of specific protein families (paragraph 3) | Discussion (paragraph 7); Materials and Methods/Additional case studies of specific protein families (paragraph 3); ambiguities: Configuration means the source-labelled evaluated identity. It does not establish missing checkpoint hashes, default settings or equivalence to same-named records in other papers.

Related records

model: TOPBP (Complex): Protein–ligand binding affinity prediction

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