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REVIEW 5 major objections 5 minor 42 references

Accurate and generalizable protein-ligand binding affinity prediction with geometric deep learning

T0 review · 5 major / 5 minor · reviewed 2026-08-16 · deepseek-v4-flash

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Pith's one-line read IPBind predicts protein-ligand binding affinity as the summed per-atom energy difference between bound and unbound states, and reports state-of-the-art generalization on proteins with low sequence identity to the training set.

desk verdict Useful architecture, but the headline generalization results are probably contaminated and the SE(3) claim is wrong as written. read the letter →

arxiv 2504.16261 v1 pith:PFIU7KJC submitted 2025-04-22 cs.CE

Krinos Li , Xianglu Xiao , Zijun Zhong , Guang Yang This is my paper · ORCID
classification cs.CE
keywords protein-ligandbindingaffinitygeometricdeeplearninginteratomicpotentialSE(3)invarianceframeaveraginggeneralizationtounseenproteinsdrugdiscovery
verification ladder T0 review T1 audit T2 compute T3 formal

The pith

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The reading

The paper proposes IPBind, a geometric deep learning model that predicts how tightly a small molecule binds a protein by summing per-atom energy differences between the protein and ligand alone and the protein–ligand complex. The central aim is to make affinity prediction generalize to proteins that share little sequence identity with the training data, where existing structure-based models degrade sharply. On the hardest 30% sequence-identity benchmark, the paper reports a Pearson correlation of 0.732, a 19.6% relative improvement over the previous best method, and reports that the model keeps its advantage when input structures are predicted by docking or co-folding rather than crystal structures. If these results hold, IPBind would give drug-discovery pipelines an affinity scorer that stays reliable for novel targets and imperfect structures.

What carries the argument

The load-bearing mechanism is the energy-difference decomposition with shared atom-level encoders. The input complex, protein pocket, and ligand are each built as atom graphs with a 5 Å radius cutoff; a common message-passing encoder produces per-atom embeddings, and an output head predicts each atom's energy contribution in the bound and unbound graphs. Binding affinity is the summed difference. Frame averaging over the four signed eigenbases of the coordinate covariance matrix enforces $SE(3)$ invariance, which the ablation table credits with up to 7.6% higher Pearson correlation than $E(3)$ invariance at 30% identity. The loss combines Balanced MSE with a ranking term, and training reaches the main result in about 14 minutes.

What would settle it

Re-run EHIGN, GIGN, and IPBind from identical code, splits, and seeds on the same 30% and 60% sequence-identity partitions; if the baseline Pearson values shift materially or complexes overlap the training set at the stated thresholds, the claimed 19.6% and 8.9% improvements are not established. A second check is to feed IPBind co-folded structures generated by a different tool than the one used in the paper; if the Pearson advantage over sequence-based baselines disappears, the robustness claim is specific to the chosen structure generator.

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Extended reading notes

Core claim

IPBind treats binding affinity as a machine-learned interatomic potential: each atom is assigned an energy contribution, and the predicted affinity is the difference between the summed atomic energies of the unbound protein plus ligand and of the bound complex. The model is made invariant to rotations and translations but not reflections, using frame averaging over signed eigenbases, so molecular chirality is respected. Trained on the PDBbind2020 refined set, it reports Pearson correlations of 0.843 at 60% sequence identity and 0.732 at 30%, surpassing listed baselines by at least 8.9% and 19.6% respectively, and remains competitive when input structures come from redocking or co-folding. The paper also shows atom-level contribution maps that align with known interaction profiler outputs, suggesting the energy-difference decomposition gives interpretable predictions.

Load-bearing premise

The central claim rests on the assumption that the low-sequence-identity test splits are truly disjoint from the training complexes at the stated identity thresholds and that the baseline numbers were obtained under the same training, validation, and evaluation protocol, so the reported relative improvements are fair.

Editorial extensions

If this is right

  • If the reported results are correct, drug-discovery pipelines can score candidate ligands against novel targets without retraining, since the model's advantage is largest exactly where sequence similarity to training data is lowest.
  • Predicted structures from redocking or co-folding can be used as input without the sharp performance drop seen for most structure-based baselines, which matters because crystal structures are rarely available early in discovery.
  • The atom-level contribution maps let a researcher see which interactions the model weights, so failures can be traced to specific missing interaction types such as water bridges.
  • The use of $SE(3)$ rather than $E(3)$ symmetry measurably improves accuracy, supporting the view that chirality-aware modeling matters for affinity.
  • Training to competitive accuracy in about 14 minutes with only atomic numbers and coordinates as input means the method can be embedded in iterative screening loops at low compute cost.

Reading between the lines

Editorial extensions of the paper, not claims the author makes directly.

  • Ablating the 50-residue pocket crop and the 5 Å edge cutoff on the 30% identity benchmark would separate the contribution of the energy-difference idea from the preprocessing choices; the paper does not report such ablations.
  • Because the model predicts per-atom energies, one can extend it to predict enthalpy-entropy components or to output an uncertainty over the summed affinity; neither is tested here.
  • The chirality claim could be probed directly with enantiomeric ligand pairs: if the $SE(3)$ model does not rank mirror-image ligands correctly, the observed gap between $SE(3)$ and $E(3)$ may come from something other than chirality-awareness.
  • The ranking loss suggests IPBind may shine more in virtual screening, where ranking candidates matters, than in absolute affinity estimation; evaluating with ranking metrics like enrichment would test that.
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Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, and a circularity audit.

Referee Report

5 major / 5 minor

Summary. The paper proposes IPBind, a geometric deep learning method for protein-ligand binding affinity prediction. The model constructs three atom-level graphs (bound complex, unbound protein pocket, unbound ligand), applies frame averaging in the preprocessing module, and predicts affinity as the sum of per-atom energy differences between bound and unbound states. The authors report state-of-the-art results on CASF2016 and on the Atom3D LBA60/LBA30 low-sequence-identity benchmarks, with claims of improved generalization to dissimilar proteins, robustness to predicted structures, interpretability, and training efficiency. The paper includes ablations on symmetry group choice, graph construction, and network architecture, as well as visualizations of atomic contributions.

Significance. If the central claims are correct, the LBA30 result (Pearson 0.732, a 19.6% relative improvement over prior methods) would be a meaningful advance in generalization for structure-based affinity prediction, and the bound/unbound potential difference is an interesting inductive bias. The paper also provides useful empirical insights, including an ablation showing that symmetry treatment and the three-graph decomposition matter for performance. The claimed robustness to predicted structures is practically important. However, the validity of the headline generalization claims depends on the LBA splits being disjoint from the PDBbind2020 training set, a point the manuscript does not verify, and on the correctness of the symmetry labeling. The empirical evidence is otherwise plausible but not fully verifiable because several key numbers are reported with zero standard deviation and robustness results appear only in figures.

major comments (5)
  1. [Section III-A and III-C, Table II] The LBA60 and LBA30 test splits are adopted from Atom3D [36], but the paper does not demonstrate that these test complexes are disjoint from the 5,050 PDBbind2020 refined-set complexes used for training IPBind. Because both the Atom3D LBA benchmark and the PDBbind2020 refined set are derived from PDBbind, the same complexes or proteins with >30% sequence identity could appear in both training and test. If so, the reported Pearson correlations of 0.843 (LBA60) and 0.732 (LBA30) and the stated 8.9% and 19.6% improvements over baselines would measure memorization rather than generalization. Please report the maximum sequence identity between IPBind's training set and each LBA test set (and for the baselines' training sets), or retrain IPBind on the original Atom3D training split and re-evaluate.
  2. [Section II-B, Eq. (3)] The frame set F_SE(3)(X) defined in Eq. (3) includes matrices with determinant -1, e.g., α=-1, β=1 gives columns [-u1, u2, -u3] with determinant -1. Averaging over this set produces output invariant to reflections, i.e., E(3)-invariant, not SE(3)-invariant. The claim that IPBind is 'SE(3)-invariant' and that this 'respects biochemical reality in chirality' is therefore not supported by the stated construction. This also confounds the interpretation of the ablation in Table III, where 'IPBind SE(3)' is compared with 'IPBind E(3)'. Please either correct the frame construction to use only proper rotations (e.g., choose frames with det=+1) or revise the symmetry claims and the discussion of chirality.
  3. [Table II] Several IPBind results are reported with zero standard deviation: 0.732±0.000 (LBA30 Pearson), 1.119±0.000 (LBA60 RMSE), and 0.843±0.000 (LBA60 Pearson). With five independent runs, an exact zero standard deviation is implausible and likely results from rounding to too few significant digits or an aggregation error. The standard deviations are needed to assess the stability of the state-of-the-art claims, particularly because the improvements over EHIGN on LBA60 are about 8.9% in Pearson. Please report means and standard deviations with sufficient precision (e.g., 0.843±0.003) and verify the computation.
  4. [Section III-D, Figure 3] The robustness experiments with redocked and co-folded structures are presented only as figures, without a numerical table. The text claims that IPBind 'uniquely matching or exceeding PSICHIC across conditions' and that GIGN/EHIGN/Tankbind suffer 'sharp performance drops' under co-folded structures, but the exact values are not given. Since this is a key practical claim, please provide a table reporting mean and standard deviation for all models under each input-structure condition, so readers can verify the comparisons.
  5. [Section III-C, Table II] The comparison with baselines is not fully controlled: only five baselines (OnionNet-2, PSICHIC, Tankbind, GIGN, EHIGN) were re-run by the authors, while the remaining numbers are 'adopted from [20]'. If those baselines were trained on a different training set or with different splits, the relative improvements may be partly due to protocol differences. Please clarify the training data and evaluation protocol for the adopted baselines, or re-run them under IPBind's training conditions.
minor comments (5)
  1. [Throughout] There are several typographical errors: 'Interatmoic Potential' in the Introduction, 'provids' in the Abstract, 'colding structures' and 'unbder' in Figure 3, and 'massage passing' in Section II-A. These should be corrected.
  2. [Eq. (4)] In the frame-averaging equation, φ takes two arguments: ((X - 1t^T)R, X). The role of the second argument X is unclear; please clarify whether it is the original coordinates used for edge construction or something else.
  3. [Section II-A] The text says 'we first cropped the protein pocket to concentrate on the most critical interaction sites, which was defined as maximum to the closest 50 residues to ligand's heavy atoms.' The wording 'maximum to' is awkward; please rephrase for clarity.
  4. [Section II-E] The total loss in Eq. (10) sums LBME and Lrank without any weighting coefficients. Please state explicitly whether equal weighting was used and whether any sensitivity analysis was performed.
  5. [References] Reference [40] appears as 'C. D. team, J. Boitreaud, ...' — the author name 'C. D. team' should be the official consortium name (e.g., Chai Discovery team). Please check the citation format.

Circularity Check

0 steps flagged · score 0.0 of 10

No circularity: IPBind is a supervised fit and its bound/unbound decomposition is an inductive bias; external benchmark evaluations do not reduce to training inputs.

full rationale

The paper's derivation chain is self-contained in the sense that matters for circularity. IPBind learns a regression from structural graphs to experimental affinity labels using Balanced MSE and ranking losses; the predicted affinity is the model output, so 'the predictions are the fit' is true of any supervised model and is not a circular reduction. The bound-versus-unbound decomposition is an architectural inductive bias, not a quantity defined by the labels. Evaluation on CASF2016 is a held-out benchmark (285 test samples removed before training), and the LBA60/LBA30 splits are adopted from Atom3D with sequence-identity restrictions. Whether those splits are fully disjoint from the PDBbind2020 training set is a data-hygiene and external-validity concern, not a by-construction equivalence between input and output. No load-bearing self-citation chain, ansatz smuggling, or renaming of a known result was found.

Assumptions & free parameters 5 free parameters · 4 assumptions · 0 invented entities

The model is supervised regression on measured affinities, so the main assumptions are the additive per-atom energy decomposition, the sufficiency of a 50-residue pocket and 5 Angstrom edge cutoff, and the integrity of the benchmark splits. There are no invented physical entities, but several hyperparameters are chosen by hand and not reported.

free parameters (5)
  • ranking loss exponent alpha = not reported
    Appears in Eq. (9) as the exponential amplification factor for the NDCG ranking loss; controls the strength of ranking supervision and is not stated in the text.
  • noise variance sigma_noise = learnable
    In Eq. (8), sigma_noise is a learnable parameter in the Balanced MSE loss; its value is not reported.
  • pocket residue cutoff = 50 residues
    The pocket is defined as the closest 50 residues to the ligand heavy atoms, chosen by hand to balance interaction coverage and efficiency (Section II-A).
  • edge radius cutoff = 5 Angstrom
    Radius edges are created at the atomic level with a 5 Angstrom cutoff (Section II-B), a hyperparameter that determines which atom pairs interact.
  • number of message passing layers = 4
    Set to 4 for each run (Section II-E), chosen without reported tuning.
assumptions (4)
  • domain assumption Binding affinity decomposes as a sum of per-atom energy contributions in bound and unbound states
    Introduced in Section II-A and II-D; the final prediction is the sum of atomic potential differences. This ignores many-body effects, solvation, and conformational entropy.
  • domain assumption The 50-residue pocket and 5 Angstrom edge cutoff capture all interactions relevant to binding affinity
    Section II-A and II-B; interactions beyond these cutoffs are discarded, which could remove long-range electrostatic or allosteric contributions.
  • standard math Frame averaging over the four PCA-based frames yields the claimed invariance and averaging is valid for non-group frame sets
    Section II-B invokes [27]; however, the chosen four frames include reflections, so the output is O(3)-invariant, not SE(3)-invariant as claimed.
  • domain assumption Atom3D LBA60 and LBA30 splits are sequence-disjoint from the training set
    Section III-A relies on these splits to measure generalization to unseen proteins; the paper does not verify overlap in the release.

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Pith. "Pith review of Accurate and generalizable protein-ligand binding affinity prediction with geometric deep learning." pith.science (2026). https://pith.science/paper/PFIU7KJC

@misc{pith2026250416261,
  author       = {Pith},
  title        = {Pith review of: Accurate and generalizable protein-ligand binding affinity prediction with geometric deep learning},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/PFIU7KJC}},
  note         = {Machine review of arXiv:2504.16261}
}
read the original abstract

Protein-ligand binding complexes are ubiquitous and essential to life. Protein-ligand binding affinity prediction (PLA) quantifies the binding strength between ligands and proteins, providing crucial insights for discovering and designing potential candidate ligands. While recent advances have been made in predicting protein-ligand complex structures, existing algorithms for interaction and affinity prediction suffer from a sharp decline in performance when handling ligands bound with novel unseen proteins. We propose IPBind, a geometric deep learning-based computational method, enabling robust predictions by leveraging interatomic potential between complex's bound and unbound status. Experimental results on widely used binding affinity prediction benchmarks demonstrate the effectiveness and universality of IPBind. Meanwhile, it provides atom-level insights into prediction. This work highlights the advantage of leveraging machine learning interatomic potential for predicting protein-ligand binding affinity.

Figures

Figures reproduced from arXiv: 2504.16261 by the authors.

Figure 1
Figure 1. Overview of IPBind A. IPbind leverages the difference between bound and unbound status of protein-ligand to predict their binding [PITH_FULL_IMAGE:figures/full_fig_p002_1.png] view at source ↗
Figure 2
Figure 2. Performance comparison between model performance under different level of test protein sequence identity. [PITH_FULL_IMAGE:figures/full_fig_p004_2.png] view at source ↗
Figure 3
Figure 3. Model performance unbder different structure input. A. Difference between crystal structure vs redocking and colding structures in [PITH_FULL_IMAGE:figures/full_fig_p005_3.png] view at source ↗
Figures from the paper (1 more)
Figure 5
Figure 5. Figure 5: Performance vs average training time over 5 runs on LBA-60 [PITH_FULL_IMAGE:figures/full_fig_p006_5.png]

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Works this paper leans on

42 extracted references · 36 canonical work pages

  1. [20]

    ProFSA: Self-supervised Pocket Pretraining via Protein Fragment-Surroundings Alignment

    B. Gao, Y . Jia, Y . Mo, Y . Ni, W. Ma, Z. Ma, and Y . Lan, “Profsa: Self-supervised pocket pretraining via protein fragment-surroundings alignment,” arXiv preprint arXiv:2310.07229 , 2023

    work page Pith review arXiv 2023

  2. [36]

    Atom3d: Tasks on molecules in three dimensions,

    R. J. Townshend, M. V ¨ogele, P. Suriana, A. Derry, A. Powers, Y . Laloudakis, S. Balachandar, B. Jing, B. Anderson, S. Eismann et al. , “Atom3d: Tasks on molecules in three dimensions,” arXiv preprint arXiv:2012.04035, 2020

    arXiv 2012

  3. [1]

    Innovation in the pharmaceutical industry: new estimates of r&d costs,

    J. A. DiMasi, H. G. Grabowski, and R. W. Hansen, “Innovation in the pharmaceutical industry: new estimates of r&d costs,” Journal of health economics, vol. 47, pp. 20–33, 2016. Technology

    work page 2016

  4. [2]

    Principles of early drug discovery,

    J. P. Hughes, S. Rees, S. B. Kalindjian, and K. L. Philpott, “Principles of early drug discovery,” British journal of pharmacology , vol. 162, no. 6, pp. 1239–1249, 2011

    work page 2011

  5. [3]

    Empirical scoring func- tions for structure-based virtual screening: applications, critical aspects, and challenges,

    I. A. Guedes, F. S. Pereira, and L. E. Dardenne, “Empirical scoring func- tions for structure-based virtual screening: applications, critical aspects, and challenges,” Frontiers in pharmacology , vol. 9, p. 1089, 2018

    work page 2018

  6. [4]

    Insights into protein–ligand interactions: mechanisms, models, and methods,

    X. Du, Y . Li, Y .-L. Xia, S.-M. Ai, J. Liang, P. Sang, X.-L. Ji, and S.-Q. Liu, “Insights into protein–ligand interactions: mechanisms, models, and methods,” International journal of molecular sciences , vol. 17, no. 2, p. 144, 2016

    work page 2016

  7. [5]

    Three-dimensional convolutional neural networks and a cross-docked data set for structure-based drug design,

    P. G. Francoeur, T. Masuda, J. Sunseri, A. Jia, R. B. Iovanisci, I. Snyder, and D. R. Koes, “Three-dimensional convolutional neural networks and a cross-docked data set for structure-based drug design,” Journal of chemical information and modeling , vol. 60, no. 9, pp. 4200–4215, 2020

    work page 2020

  8. [6]

    Current trends in computer aided drug design and a highlight of drugs discovered via computational techniques: A review,

    V . T. Sabe, T. Ntombela, L. A. Jhamba, G. E. Maguire, T. Govender, T. Naicker, and H. G. Kruger, “Current trends in computer aided drug design and a highlight of drugs discovered via computational techniques: A review,” European Journal of Medicinal Chemistry , vol. 224, p. 113705, 2021

    work page 2021

Show all 42 references
  1. [7]

    Deepdta: deep drug–target binding affinity prediction,

    H. ¨Ozt¨urk, A. ¨Ozg¨ur, and E. Ozkirimli, “Deepdta: deep drug–target binding affinity prediction,” Bioinformatics, vol. 34, no. 17, pp. i821– i829, 2018

    2018

  2. [8]

    Graphdta: predicting drug–target binding affinity with graph neural networks,

    T. Nguyen, H. Le, T. P. Quinn, T. Nguyen, T. D. Le, and S. Venkatesh, “Graphdta: predicting drug–target binding affinity with graph neural networks,” Bioinformatics, vol. 37, no. 8, pp. 1140–1147, 2021

    2021

  3. [9]

    Deep- purpose: a deep learning library for drug–target interaction prediction,

    K. Huang, T. Fu, L. M. Glass, M. Zitnik, C. Xiao, and J. Sun, “Deep- purpose: a deep learning library for drug–target interaction prediction,” Bioinformatics, vol. 36, no. 22-23, pp. 5545–5547, 2020

    2020

  4. [10]

    Multi-scale representation learning on proteins,

    V . R. Somnath, C. Bunne, and A. Krause, “Multi-scale representation learning on proteins,” Advances in Neural Information Processing Sys- tems, vol. 34, pp. 25 244–25 255, 2021

    2021

  5. [11]

    Development and evaluation of a deep learning model for protein–ligand binding affinity prediction,

    M. M. Stepniewska-Dziubinska, P. Zielenkiewicz, and P. Siedlecki, “Development and evaluation of a deep learning model for protein–ligand binding affinity prediction,” Bioinformatics, vol. 34, no. 21, pp. 3666– 3674, 2018

    2018

  6. [12]

    K deep: protein–ligand absolute binding affinity prediction via 3d-convolutional neural networks,

    J. Jim ´enez, M. Skalic, G. Martinez-Rosell, and G. De Fabritiis, “K deep: protein–ligand absolute binding affinity prediction via 3d-convolutional neural networks,” Journal of chemical information and modeling , vol. 58, no. 2, pp. 287–296, 2018

    2018

  7. [13]

    Ss- gnn: a simple-structured graph neural network for affinity prediction,

    S. Zhang, Y . Jin, T. Liu, Q. Wang, Z. Zhang, S. Zhao, and B. Shan, “Ss- gnn: a simple-structured graph neural network for affinity prediction,” ACS omega, vol. 8, no. 25, pp. 22 496–22 507, 2023

    2023

  8. [14]

    Tankbind: Trigonometry-aware neural networks for drug-protein binding structure prediction,

    W. Lu, Q. Wu, J. Zhang, J. Rao, C. Li, and S. Zheng, “Tankbind: Trigonometry-aware neural networks for drug-protein binding structure prediction,” Advances in neural information processing systems , vol. 35, pp. 7236–7249, 2022

    2022

  9. [15]

    Highly accurate quantum chemical property prediction with uni-mol+,

    S. Lu, Z. Gao, D. He, L. Zhang, and G. Ke, “Highly accurate quantum chemical property prediction with uni-mol+,” arXiv preprint arXiv:2303.16982, 2023

    2023 arXiv

  10. [16]

    Physic- ochemical graph neural network for learning protein–ligand interaction fingerprints from sequence data,

    H. Y . Koh, A. T. Nguyen, S. Pan, L. T. May, and G. I. Webb, “Physic- ochemical graph neural network for learning protein–ligand interaction fingerprints from sequence data,” Nature Machine Intelligence , vol. 6, no. 6, pp. 673–687, 2024

    2024

  11. [17]

    The future of machine learning for small-molecule drug discovery will be driven by data,

    G. Durant, F. Boyles, K. Birchall, and C. M. Deane, “The future of machine learning for small-molecule drug discovery will be driven by data,” Nature computational science , pp. 1–9, 2024

    2024

  12. [18]

    Artificial intelligence in drug development,

    K. Zhang, X. Yang, Y . Wang, Y . Yu, N. Huang, G. Li, X. Li, J. C. Wu, and S. Yang, “Artificial intelligence in drug development,” Nature Medicine, pp. 1–15, 2025

    2025

  13. [19]

    Learning hierarchical protein representations via complete 3d graph networks,

    L. Wang, H. Liu, Y . Liu, J. Kurtin, and S. Ji, “Learning hierarchical protein representations via complete 3d graph networks,” arXiv preprint arXiv:2207.12600, 2022

    2022 arXiv

  14. [21]

    Protein structure generation via folding diffusion,

    K. E. Wu, K. K. Yang, R. van den Berg, S. Alamdari, J. Y . Zou, A. X. Lu, and A. P. Amini, “Protein structure generation via folding diffusion,” Nature communications, vol. 15, no. 1, p. 1059, 2024

    2024

  15. [22]

    Geometric interaction graph neural network for predicting protein–ligand binding affinities from 3d structures (gign),

    Z. Yang, W. Zhong, Q. Lv, T. Dong, and C. Yu-Chian Chen, “Geometric interaction graph neural network for predicting protein–ligand binding affinities from 3d structures (gign),” The journal of physical chemistry letters, vol. 14, no. 8, pp. 2020–2033, 2023

    2020

  16. [23]

    Schnet–a deep learning architecture for molecules and materials,

    K. T. Sch ¨utt, H. E. Sauceda, P.-J. Kindermans, A. Tkatchenko, and K.-R. M ¨uller, “Schnet–a deep learning architecture for molecules and materials,” The Journal of Chemical Physics , vol. 148, no. 24, 2018

    2018

  17. [24]

    Extending machine learning beyond interatomic potentials for predicting molecular properties,

    N. Fedik, R. Zubatyuk, M. Kulichenko, N. Lubbers, J. S. Smith, B. Nebgen, R. Messerly, Y . W. Li, A. I. Boldyrev, K. Barros et al. , “Extending machine learning beyond interatomic potentials for predicting molecular properties,” Nature Reviews Chemistry , vol. 6, no. 9, pp. 65...

    2022

  18. [25]

    Mace: Higher order equivariant message passing neural networks for fast and accurate force fields,

    I. Batatia, D. P. Kovacs, G. Simm, C. Ortner, and G. Cs ´anyi, “Mace: Higher order equivariant message passing neural networks for fast and accurate force fields,” Advances in neural information processing systems, vol. 35, pp. 11 423–11 436, 2022

    2022

  19. [26]

    E (3)-equivariant graph neural networks for data-efficient and accurate interatomic potentials,

    S. Batzner, A. Musaelian, L. Sun, M. Geiger, J. P. Mailoa, M. Kornbluth, N. Molinari, T. E. Smidt, and B. Kozinsky, “E (3)-equivariant graph neural networks for data-efficient and accurate interatomic potentials,” Nature communications, vol. 13, no. 1, p. 2453, 2022

    2022

  20. [27]

    Frame averaging for invariant and equivariant network design,

    O. Puny, M. Atzmon, H. Ben-Hamu, I. Misra, A. Grover, E. J. Smith, and Y . Lipman, “Frame averaging for invariant and equivariant network design,” arXiv preprint arXiv:2110.03336 , 2021

    2021 arXiv

  21. [28]

    A hitchhiker’s guide to geometric gnns for 3d atomic systems,

    A. Duval, S. V . Mathis, C. K. Joshi, V . Schmidt, S. Miret, F. D. Malliaros, T. Cohen, P. Li `o, Y . Bengio, and M. Bronstein, “A hitchhiker’s guide to geometric gnns for 3d atomic systems,” arXiv preprint arXiv:2312.07511, 2023

    2023 arXiv

  22. [29]

    Faenet: Frame averaging equivariant gnn for materials modeling,

    A. A. Duval, V . Schmidt, A. Hern´andez-Garcıa, S. Miret, F. D. Malliaros, Y . Bengio, and D. Rolnick, “Faenet: Frame averaging equivariant gnn for materials modeling,” in International Conference on Machine Learning . PMLR, 2023, pp. 9013–9033

    2023

  23. [30]

    Balanced mse for imbalanced visual regression,

    J. Ren, M. Zhang, C. Yu, and Z. Liu, “Balanced mse for imbalanced visual regression,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , 2022, pp. 7926–7935

    2022

  24. [31]

    Learning to rank by optimizing ndcg measure,

    H. Valizadegan, R. Jin, R. Zhang, and J. Mao, “Learning to rank by optimizing ndcg measure,” Advances in neural information processing systems, vol. 22, 2009

    2009

  25. [32]

    Onionnet-2: a convolutional neural network model for predicting protein-ligand binding affinity based on residue-atom contacting shells,

    Z. Wang, L. Zheng, Y . Liu, Y . Qu, Y .-Q. Li, M. Zhao, Y . Mu, and W. Li, “Onionnet-2: a convolutional neural network model for predicting protein-ligand binding affinity based on residue-atom contacting shells,” Frontiers in chemistry , vol. 9, p. 753002, 2021

    2021

  26. [33]

    Interaction-based inductive bias in graph neural networks: enhancing protein-ligand binding affinity predictions from 3d structures,

    Z. Yang, W. Zhong, Q. Lv, T. Dong, G. Chen, and C. Y .-C. Chen, “Interaction-based inductive bias in graph neural networks: enhancing protein-ligand binding affinity predictions from 3d structures,” IEEE Transactions on Pattern Analysis and Machine Intelligence , 2024

    2024

  27. [34]

    Comparative assessment of scoring functions: the casf-2016 update,

    M. Su, Q. Yang, Y . Du, G. Feng, Z. Liu, Y . Li, and R. Wang, “Comparative assessment of scoring functions: the casf-2016 update,” Journal of chemical information and modeling , vol. 59, no. 2, pp. 895– 913, 2018

    2016

  28. [35]

    Latent biases in machine learning models for predicting binding affinities using popular data sets,

    G. C. Kanakala, R. Aggarwal, D. Nayar, and U. D. Priyakumar, “Latent biases in machine learning models for predicting binding affinities using popular data sets,” ACS omega, vol. 8, no. 2, pp. 2389–2397, 2023

    2023

  29. [37]

    Structure-aware interactive graph neural networks for the prediction of protein-ligand binding affinity,

    S. Li, J. Zhou, T. Xu, L. Huang, F. Wang, H. Xiong, W. Huang, D. Dou, and H. Xiong, “Structure-aware interactive graph neural networks for the prediction of protein-ligand binding affinity,” in Proceedings of the 27th ACM SIGKDD conference on knowledge discovery & data mining ...

    2021

  30. [38]

    Molecular recognition: lock-and-key, induced fit, and conformational selection,

    T. Holyoak, “Molecular recognition: lock-and-key, induced fit, and conformational selection,” Encyclopedia of biophysics , pp. 1584–1588, 2013

    2013

  31. [39]

    Autodock vina 1.2. 0: New docking methods, expanded force field, and python bindings,

    J. Eberhardt, D. Santos-Martins, A. F. Tillack, and S. Forli, “Autodock vina 1.2. 0: New docking methods, expanded force field, and python bindings,” Journal of chemical information and modeling , vol. 61, no. 8, pp. 3891–3898, 2021

    2021

  32. [40]

    Chai-1: Decoding the molecular interactions of life,

    C. D. team, J. Boitreaud, J. Dent, M. McPartlon, J. Meier, V . Reis, A. Ro- gozhonikov, and K. Wu, “Chai-1: Decoding the molecular interactions of life,” BioRxiv, pp. 2024–10, 2024

    2024

  33. [41]

    Accurate structure prediction of biomolecular interactions with alphafold 3,

    J. Abramson, J. Adler, J. Dunger, R. Evans, T. Green, A. Pritzel, O. Ronneberger, L. Willmore, A. J. Ballard, J. Bambrick et al., “Accurate structure prediction of biomolecular interactions with alphafold 3,” Nature, vol. 630, no. 8016, pp. 493–500, 2024

    2024

  34. [42]

    Plip: fully automated protein–ligand interaction profiler,

    S. Salentin, S. Schreiber, V . J. Haupt, M. F. Adasme, and M. Schroeder, “Plip: fully automated protein–ligand interaction profiler,” Nucleic acids research, vol. 43, no. W1, pp. W443–W447, 2015

    2015

Pith tools

Reviewed August 16, 2026 · model on record in the stance chip above.