Configuration

DeepDTA (ATOM3D baseline)

DeepDTA predicts drug-target binding affinity from a ligand SMILES string and a protein sequence, using a separate 1D convolutional encoder for each.

Identified as DeepDTA. The source table prints [Öztürk et al., 2018].

Configuration: Retrained by the ATOM3D authors with the original paper's hyperparameters: regression for LBA and binary classification for LEP.

ATOM3D §5.1 compares to 'DeepDTA [Öztürk et al., 2018]', a 1DCNN over protein sequence and ligand SMILES, and App. F.5 and F.6 link the public DeepDTA repository. The cited paper introduces DeepDTA as two 1D CNN blocks over SMILES and protein sequence. The identity comes from this text, not from the author string or a matching score.

Training: Trained by the ATOM3D authors with the same hyperparameters as the original paper.
Tasks: LBA as regression. For LEP the task is reduced to binary classification of a protein sequence and ligand SMILES pair, without a twin network.
LBA split: The Table 5 LBA values equal the Table 8 DeepDTA row for LBA (30%).

Not established: Dataset-specific settings. The original paper uses different maximum input lengths for Davis and KIBA. ATOM3D does not say which settings it used. Checkpoint. No retrained ATOM3D weights or checkpoint are identified. Implementation revision. ATOM3D links the repository without a commit.

ATOM3D: Tasks On Molecules in Three Dimensions; DeepDTA: Deep Drug-Target Binding Affinity Prediction (arXiv:1801.10193v2); DeepDTA repository README (hkmztrk/DeepDTA) · ATOM3D arXiv:2012.04035v4 §5.1 and Table 5, p.7; App. F.5 and F.6 with footnote 10, p.24; Table 8 LBA (30%) and LEP rows, p.28. DeepDTA arXiv:1801.10193v2 title and abstract, p.1; Proposed model and Figure 2, PDF pp.7-8. github.com/hkmztrk/DeepDTA README at a546a8433a6822e958f36171c4356ad6f414d623.. AI-assisted source review on 2026-09-24; no human scientific review.

Sources

DeepDTA: Deep Drug-Target Binding Affinity Prediction (arXiv:1801.10193v2) · arXiv:1801.10193v2 abstract, p.1; Proposed model and Figure 2, PDF pp.7-8

4 evaluations · 4 results

Overview

Model type

Two 1D convolutional encoders with a fully connected regression head

Sources

DeepDTA: Deep Drug-Target Binding Affinity Prediction (arXiv:1801.10193v2) · arXiv:1801.10193v2 Proposed model and Figure 2, PDF pp.7-8

Inputs

Ligand SMILES string and protein amino-acid sequence

Sources

DeepDTA: Deep Drug-Target Binding Affinity Prediction (arXiv:1801.10193v2) · arXiv:1801.10193v2 abstract, p.1; Proposed model, PDF p.7

Output

Continuous binding affinity, trained with mean squared error

Sources

DeepDTA: Deep Drug-Target Binding Affinity Prediction (arXiv:1801.10193v2) · arXiv:1801.10193v2 Proposed model, PDF pp.7-8

limited source coverage · Automated source review, 2026-09-24. All specifications and missing details

Evaluations and results

4 evaluations · 4 results. Different protocols are not a single leaderboard.

Filter evaluations

Tested model or methodProtocolDatasetEvaluation setupMetricEvidence origin

Applied filters: All linked evaluations

Exact evaluated configurations and original reported results
Tested configuration	Protocol and dataset	Finding	Evidence and details
Configuration: DeepDTA (ATOM3D baseline) (cited as [Öztürk et al., 2018])	Task: ATOM3D LBA-RMSE: Ligand binding affinity, root mean squared error Dataset subset: ATOM3D LBA (ATOM3D split)	1.56 rmse error · lower Uncertainty: Not reported Coverage: Not reported scored / Not reported eligible	Author-reported evaluation · Source checked Methods, coverage and source DeepDTA (ATOM3D baseline) on ATOM3D LBA-RMSE: Ligand binding affinity, root mean squared error Trained and scored under the ATOM3D split for this task. Asterisks in the paper mark a run whose training data differed. Aggregation: Not reported ATOM3D: Tasks On Molecules in Three Dimensions · Table 5, row(LBA RMSE), column([Öztürk et al., 2018])
Configuration: DeepDTA (ATOM3D baseline) (cited as [Öztürk et al., 2018])	Task: ATOM3D LBA-RP: Ligand binding affinity, global Pearson correlation Dataset subset: ATOM3D LBA (ATOM3D split)	0.573 pearson_r correlation · higher Uncertainty: Not reported Coverage: Not reported scored / Not reported eligible	Author-reported evaluation · Source checked Methods, coverage and source DeepDTA (ATOM3D baseline) on ATOM3D LBA-RP: Ligand binding affinity, global Pearson correlation Trained and scored under the ATOM3D split for this task. Asterisks in the paper mark a run whose training data differed. Aggregation: Not reported ATOM3D: Tasks On Molecules in Three Dimensions · Table 5, row(glob. RP), column([Öztürk et al., 2018])
Configuration: DeepDTA (ATOM3D baseline) (cited as [Öztürk et al., 2018])	Task: ATOM3D LBA-RS: Ligand binding affinity, global Spearman correlation Dataset subset: ATOM3D LBA (ATOM3D split)	0.574 spearman_r correlation · higher Uncertainty: Not reported Coverage: Not reported scored / Not reported eligible	Author-reported evaluation · Source checked Methods, coverage and source DeepDTA (ATOM3D baseline) on ATOM3D LBA-RS: Ligand binding affinity, global Spearman correlation Trained and scored under the ATOM3D split for this task. Asterisks in the paper mark a run whose training data differed. Aggregation: Not reported ATOM3D: Tasks On Molecules in Three Dimensions · Table 5, row(glob. RS), column([Öztürk et al., 2018])
Configuration: DeepDTA (ATOM3D baseline) (cited as [Öztürk et al., 2018])	Task: ATOM3D LEP-AUROC: Ligand efficacy prediction Dataset subset: ATOM3D LEP (ATOM3D split)	0.696 auroc fraction · higher Uncertainty: Not reported Coverage: Not reported scored / Not reported eligible	Author-reported evaluation · Source checked Methods, coverage and source DeepDTA (ATOM3D baseline) on ATOM3D LEP-AUROC: Ligand efficacy prediction Trained and scored under the ATOM3D split for this task. Asterisks in the paper mark a run whose training data differed. Aggregation: Not reported ATOM3D: Tasks On Molecules in Three Dimensions · Table 5, row(LEP AUROC), column([Öztürk et al., 2018])

Source checking is not independent reproduction. Release 2026-09-29-06401fd5b220.

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How it works, versions and access

Related profile: DeepDTA. This page retains the exact record and its evaluation context.

This configuration

DeepDTA, a one-dimensional convolutional network over both partners, as the ATOM3D text describes it.

record: DeepDTA (ATOM3D baseline)
configuration: Not reported
entity type: Configuration

How it works

SMILES strings and protein sequences are label-encoded characters. Each passes through its own block of three 1D convolutional layers, where the second and third layers have two and three times the filters of the first, followed by max-pooling. The two pooled vectors are concatenated and fed into fully connected layers of 1,024, 1,024 and 512 units, with dropout after the first two, and a regression output trained with mean squared error.

Sources

DeepDTA: Deep Drug-Target Binding Affinity Prediction (arXiv:1801.10193v2) · arXiv:1801.10193v2 input representation, PDF p.4; Proposed model and Figure 2, PDF pp.7-8

Original evaluation

The paper evaluates on the Davis kinase dataset (Kd) and the KIBA dataset. It fixes maximum lengths of 85 SMILES and 1,200 protein characters for Davis and 100 and 1,000 for KIBA.

Sources

DeepDTA: Deep Drug-Target Binding Affinity Prediction (arXiv:1801.10193v2) · arXiv:1801.10193v2 Datasets and Table 1, PDF p.3; input representation, PDF p.6

Strengths, limitations and unresolved questions

Strengths and limitations

Strengths and considerations

No source-reviewed explanatory claims are recorded here yet.

Limitations and conditions

Uses only the 1D sequence and SMILES strings, not the 3D structure of the complex.
Sources
DeepDTA: Deep Drug-Target Binding Affinity Prediction (arXiv:1801.10193v2) · arXiv:1801.10193v2 abstract, p.1

Profile review details

AI-assisted review of the cited claims against pinned primary sources. No human scientific review and no independent reproduction.

Stable record: identity-model-deepdta

Specifications

Inputs, training, access and other details

Explanatory profile: limited source coverage · Automated source review, 2026-09-24. 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	Two 1D convolutional encoders with a fully connected regression head Sources DeepDTA: Deep Drug-Target Binding Affinity Prediction (arXiv:1801.10193v2) · arXiv:1801.10193v2 Proposed model and Figure 2, PDF pp.7-8
Inputs	Ligand SMILES string and protein amino-acid sequence Sources DeepDTA: Deep Drug-Target Binding Affinity Prediction (arXiv:1801.10193v2) · arXiv:1801.10193v2 abstract, p.1; Proposed model, PDF p.7
Output	Continuous binding affinity, trained with mean squared error Sources DeepDTA: Deep Drug-Target Binding Affinity Prediction (arXiv:1801.10193v2) · arXiv:1801.10193v2 Proposed model, PDF pp.7-8
Original datasets	Davis (Kd) and KIBA Sources DeepDTA: Deep Drug-Target Binding Affinity Prediction (arXiv:1801.10193v2) · arXiv:1801.10193v2 Datasets and Table 1, PDF p.3
Code	github.com/hkmztrk/DeepDTA Sources DeepDTA repository README (hkmztrk/DeepDTA) · README at a546a8433a6822e958f36171c4356ad6f414d623
Parameter count	Not extracted · Needs further source review Sources DeepDTA: Deep Drug-Target Binding Affinity Prediction (arXiv:1801.10193v2) · arXiv:1801.10193v2
Known versions	Not extracted or verified for this record.
Training data	Not extracted or verified for this record.
Context limits	Not extracted or verified for this record.
Access	Not extracted or verified for this record.
Code licence	Not extracted or verified for this record.
Weights licence	Not extracted or verified for this record.

Evidence

Source checking verifies the cited claim or transcription. It does not establish independent reproduction.

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.

2 evidence rows matching the loaded filters

Claims, original sources and review scope · Release 2026-09-29-06401fd5b220
Property and statement	Original source and location	Review and provenance
Relationship: family identity-model-deepdta Individual claims	ATOM3D: Tasks On Molecules in Three Dimensions Original source ↗ ATOM3D §5.1, p.7; App. F.5 and F.6, p.24. DeepDTA arXiv:1801.10193v2 Proposed model, PDF pp.7-8. Shared locator for this statement’s cited sources; not a separate locator for each citation. Version: Primary full-text snapshot retrieved 2026-09-17; exact bytes pinned by SHA-256 Retrieved: 2026-09-17T08:06:28.182997+00:00	source checked automated source review · 2026-09-24 Audit details Source review establishes this relationship only. Exact evaluated configurations and original numerical review status remain unchanged. ATOM3D retrained DeepDTA with the original hyperparameters. Family attribution only: the retrained weights and their LBA and LEP scores stay with this configuration. Field: `links:family:identity-model-deepdta` Claim: model-evaluation-identity-7a1a564899b024c2ca64 Source artifact SHA-256: `92656c20a15311c32bed9edc7f465bb26eb30338f40324fe43bec4b1fc6a7890` Hash scope: Exact retrieved primary paper artifact bytes. Inspected artifact
Relationship: family identity-model-deepdta Individual claims	DeepDTA: Deep Drug-Target Binding Affinity Prediction (arXiv:1801.10193v2) Original source ↗ ATOM3D §5.1, p.7; App. F.5 and F.6, p.24. DeepDTA arXiv:1801.10193v2 Proposed model, PDF pp.7-8. Shared locator for this statement’s cited sources; not a separate locator for each citation. Version: 1801.10193v2 Retrieved: 2026-09-24T19:28:11+00:00	source checked automated source review · 2026-09-24 Audit details Source review establishes this relationship only. Exact evaluated configurations and original numerical review status remain unchanged. ATOM3D retrained DeepDTA with the original hyperparameters. Family attribution only: the retrained weights and their LBA and LEP scores stay with this configuration. Field: `links:family:identity-model-deepdta` Claim: model-evaluation-identity-7a1a564899b024c2ca64 Source artifact SHA-256: `3c368fed02e7ac774a8249c9551ede2e3a2c5d6e14382667f8fade87bbde796e` Hash scope: SHA-256 of the retrieved original artifact bytes Inspected artifact

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Sources and history

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Release 2026-09-29-06401fd5b220 · Record review: source checked

3 source records and release history

ATOM3D: Tasks On Molecules in Three Dimensions · Original source · Primary full-text snapshot retrieved 2026-09-17; exact bytes pinned by SHA-256
DeepDTA: Deep Drug-Target Binding Affinity Prediction (arXiv:1801.10193v2) · Original source · 1801.10193v2
DeepDTA repository README (hkmztrk/DeepDTA) · Original source · a546a8433a6822e958f36171c4356ad6f414d623

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Technical metadata and extraction receipts

Stable ID: atom3d-method-zt-rk-et-al-2018

areas: molecular-interactions
source locator: Table 5, column([Öztürk et al., 2018])
missing metadata: checkpoint revision: unreported; parameters: unextracted
source label: [Öztürk et al., 2018]
source identity: status: resolved; label form: bracketed_citation; display name: DeepDTA (ATOM3D baseline); identity: DeepDTA; configuration: Retrained by the ATOM3D authors with the original paper's hyperparameters: regression for LBA and binary classification for LEP.; basis: ATOM3D §5.1 compares to 'DeepDTA [Öztürk et al., 2018]', a 1DCNN over protein sequence and ligand SMILES, and App. F.5 and F.6 link the public DeepDTA repository. The cited paper introduces DeepDTA as two 1D CNN blocks over SMILES and protein sequence. The identity comes from this text, not from the author string or a matching score.; source ids: evidence-expansion-atom3d-92656c20; source-label-deepdta-arxiv-3c368fed; source-label-deepdta-readme-a546a843; source locator: ATOM3D arXiv:2012.04035v4 §5.1 and Table 5, p.7; App. F.5 and F.6 with footnote 10, p.24; Table 8 LBA (30%) and LEP rows, p.28. DeepDTA arXiv:1801.10193v2 title and abstract, p.1; Proposed model and Figure 2, PDF pp.7-8. github.com/hkmztrk/DeepDTA README at a546a8433a6822e958f36171c4356ad6f414d623.; known details: label: Training; value: Trained by the ATOM3D authors with the same hyperparameters as the original paper.; source ids: evidence-expansion-atom3d-92656c20; source locator: ATOM3D App. F.5 and F.6, p.24; label: Tasks; value: LBA as regression. For LEP the task is reduced to binary classification of a protein sequence and ligand SMILES pair, without a twin network.; source ids: evidence-expansion-atom3d-92656c20; source locator: ATOM3D App. F.5 and F.6, p.24; label: LBA split; value: The Table 5 LBA values equal the Table 8 DeepDTA row for LBA (30%).; source ids: evidence-expansion-atom3d-92656c20; source locator: ATOM3D Table 5, p.7; Table 8, p.28; unknown: label: Dataset-specific settings; note: The original paper uses different maximum input lengths for Davis and KIBA. ATOM3D does not say which settings it used.; label: Checkpoint; note: No retrained ATOM3D weights or checkpoint are identified.; label: Implementation revision; note: ATOM3D links the repository without a commit.; original name: [Öztürk et al., 2018]; original description: DeepDTA, a one-dimensional convolutional network over both partners, as the ATOM3D text describes it.; review: method: automated_source_review; date: 2026-09-24; note: AI-assisted review against the cited primary sources. No human scientific review. Values, locators and comparison conditions are unchanged.

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