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Graph Neural Networks in Modern AI-aided Drug Discovery

Odin Zhang, Haitao Lin, Xujun Zhang, Xiaorui Wang, Zhenxing Wu, Qing Ye, Weibo Zhao, Jike Wang, Kejun Ying, Yu Kang, Chang-yu Hsieh, Tingjun Hou

arXiv:2506.06915v1q-bio.BMcs.LG

TL;DR

Drug discovery requires methods that can address costly, complex molecular and biological design problems across multiple pipeline stages. This review organizes GNN foundations and applications across drug-discovery tasks, emphasizing geometric, interpretable, scalable, generative, and integrated learning approaches. It concludes that GNNs can serve as relational bridges connecting chemical structures with biological modalities toward rational discovery of safe, efficacious therapeutics, while practical limitations remain.

  • Problem

    Drug discovery is resource-intensive and time-consuming, while applying GNNs across real-world pipelines involves practical challenges and methodological bottlenecks.

  • Method

    The review provides a model-centric synthesis of GNN foundations, applications, methodological advances, and integration with contemporary deep learning paradigms in AIDD.

  • Results

    GNNs can act as relational bridges connecting chemical structures to biological modalities that govern therapeutic response, supporting rational discovery of safe, efficacious therapeutics.

  • Takeaways & Limitations

    GNNs provide a connective modeling framework for integrating chemical structures with broader biological information across drug-discovery applications.

  • Takeaways & Limitations

    Pretraining and transfer learning can underperform task-specific baselines through negative transfer, and synthesis modeling faces additional design challenges from reaction components such as catalysts and solvents.

Abstract

from arXiv · show

Graph neural networks (GNNs), as topology/structure-aware models within deep learning, have emerged as powerful tools for AI-aided drug discovery (AIDD). By directly operating on molecular graphs, GNNs offer an intuitive and expressive framework for learning the complex topological and geometric features of drug-like molecules, cementing their role in modern molecular modeling. This review provides a comprehensive overview of the methodological foundations and representative applications of GNNs in drug discovery, spanning tasks such as molecular property prediction, virtual screening, molecular generation, biomedical knowledge graph construction, and synthesis planning. Particular attention is given to recent methodological advances, including geometric GNNs, interpretable models, uncertainty quantification, scalable graph architectures, and graph generative frameworks. We also discuss how these models integrate with modern deep learning approaches, such as self-supervised learning, multi-task learning, meta-learning and pre-training. Throughout this review, we highlight the practical challenges and methodological bottlenecks encountered when applying GNNs to real-world drug discovery pipelines, and conclude with a discussion on future directions.

1. Introduction

Drug discovery is costly and time-consuming, motivating AI-aided, data-driven approaches that use molecular representations suited to chemical structure. This review presents GNNs as a model-centric framework spanning diverse drug-discovery tasks and recent advances.

  • Motivation: Drug development typically requires more than a decade and over two billion US dollars, creating strong motivation for more efficient computational approaches.AI-aided drug discovery is presented as a system-level paradigm covering target identification, hit discovery, lead optimization, and early risk mitigation.
  • Motivation: AI is accelerating drug discovery’s transition from rule-based heuristics toward data-driven strategies supported by expanding structural and multi-omics datasets.The review describes resources including PDB and TCGA as foundations for data-driven modeling.
  • Molecular representations: Existing molecular representations trade off expressiveness and structural fidelity: expert descriptors depend on predefined patterns, while strings struggle with symmetry, reversibility, and long-range dependencies.Fingerprints and descriptors are also described as limited in expressiveness, whereas graph representations preserve molecular topology and geometry.
  • GNN foundations: GNNs operate on atoms and bonds, using message passing and aggregation to encode molecular semantics while geometric operators model stereochemistry, charge distributions, and spatial configurations.The review highlights equivariant or invariant GNNs, including SO(3)-equivariant models, for symmetry-aware molecular modeling.
  • GNN foundations: Graph representations support physical consistency, cross-task applicability, and interpretability through structural attribution mechanisms.The review connects these properties to tasks including property prediction, generative modeling, and pharmacological profiling.
  • Scope and organization: The review adopts a model-centric perspective across property prediction, virtual screening, molecular generation, knowledge graphs, and synthesis planning.It emphasizes geometric deep learning, large-scale architectures, interpretability, uncertainty quantification, graph generation, reinforcement learning, and integration with self-supervised, multi-task, pretraining, and meta-learning methods.
  • Scope and organization: By linking structural dependencies across seemingly different tasks, the review aims to provide a coherent framework for adapting GNN techniques to drug-discovery challenges.The stated audience includes researchers applying AI in chemistry and biology and computational scientists developing biochemical models.

2.1 General Graph Theory

GNNs are organized into spectral-based methods that filter graph signals in the Laplacian eigenbasis and spatial-based methods that aggregate neighborhood information directly. Spatial methods, especially MPNN frameworks, dominate chemistry-related applications because they offer efficient, intuitive local interaction modeling.

  • Spectral-based GNNs: Spectral-based GNNs define convolution through graph Laplacian eigendecomposition and learnable filters in the graph Fourier domain.They project node features into the spectral domain, reweight spectral components, and project the result back to the node domain.
  • Spatial-based GNNs: Spatial-based graph convolution has become the predominant paradigm in chemistry-related tasks because of its conceptual clarity, ecosystem support, and efficient local modeling.Libraries such as PyG and DGL facilitate practical implementation of these frameworks.
  • Spatial-based GNNs: Spatial-based GNNs propagate and aggregate messages directly across graph neighborhoods using learnable local interaction functions.The Message Passing Neural Networks framework provides a general abstraction for many spatial architectures, including GraphSAGE, GIN, and GAT.
  • Representative spectral models: SCNN faces O(n^3) eigendecomposition cost and O(n) parameter growth, limiting scalability and increasing overfitting risk on large graphs.Its eigendecomposition-based design is also described as relatively inflexible.
  • Representative spectral models: ChebNet replaces independently learned spectral entries with a shared polynomial filter whose parameter count depends on polynomial order K rather than graph size.Its K-hop receptive field enforces spatial locality while improving scalability and reducing overfitting risks.
  • Representative spatial models: GIN can match the WL test in distinguishing graph structures, making it suitable for tasks requiring high discriminative power.Its update includes a learnable scalar controlling the contribution of a node’s own features.

2.2 Symmetric GNN: Incorporate Physical Laws

Symmetric GNNs incorporate molecular geometry and physical symmetries so predictions respect transformations of 3D molecular structures. The section organizes invariant and equivariant approaches by their geometric representations and interaction orders.

  • Motivation: Molecular geometries combine 2D topological structures with 3D conformations, while relative atomic arrangements remain physically unchanged across reference frames.
  • Invariance and equivariance: Invariant models keep scalar outputs unchanged under coordinate transformations, whereas equivariant models transform vectorial or tensorial outputs consistently with rotated or translated inputs.
  • Invariance and equivariance: MLPs do not inherently satisfy these symmetry constraints, motivating physically informed inductive biases and symmetry-aware message-passing architectures.
  • Internal coordinate-based invariant GNN: SchNet uses distance-based continuous filters and radial basis functions to provide SE(3)-invariant scalar predictions and equivariant force prediction through energy gradients.
  • Internal coordinate-based invariant GNN: GemNet extends directional message passing with third-order interactions and improves performance over DimeNet by approximately 2×, but quadruplet enumeration increases computational cost and limits scalability.

2.3 Large and Deep GNN: Challenges and Actions

Scaling GNNs to larger and deeper architectures is limited by depth-related optimization and representation problems, scarce labeled data, and pretraining misalignment. The review surveys architectural, Transformer-based, and self-supervised strategies intended to address these bottlenecks, including scalable Graph Transformers and geometry-aware pretraining.

  • Scaling motivation: Larger models can improve representation learning and generalization, but real-world graph domains often lack enough labeled data to exploit their capacity.The section motivates unlabeled-data pretraining as a response to limited supervision.
  • Review scope: The review organizes scaling solutions around architectural redesign, Transformer integration, and generative, predictive, and contrastive pretraining on unlabeled graphs.These solution families are summarized as the chapter’s main focus and in Table 3.
  • Architectural challenges: Deepening GNNs does not reliably improve performance because over-smoothing, over-squashing, and gradient anomalies constrain deeper architectures.Over-smoothing homogenizes node representations and reduces their discriminability.
  • Graph Transformers: GraphGPS combines local message passing with global attention and achieves linear complexity O(N+E) while retaining expressivity.The framework is presented as a flexible blueprint for graph-based learning tasks.

2.4 Graph Generative Models and Probability Learning

Graph generative models address molecular graph generation through probabilistic models that learn distributions or latent representations, and graph-editing models that modify structures sequentially. These approaches must handle variable-sized, high-dimensional, nonlinear molecular graphs while balancing expressivity, tractability, and sampling efficiency.

  • Challenges: Molecular graph generation is challenging because molecules contain variable numbers of atoms and bonds, creating a non-fixed-length decision space.The review identifies this variable structure as a non-trivial modeling problem.
  • Graph Generative Approaches: Graph embedding approaches encode whole molecular graphs into continuous latent representations and decode them into graph structures in one step or autoregressively.These methods are closely linked to probabilistic generative modeling.
  • Probability Learning: Likelihood-based models explicitly learn data distributions and generate samples from the learned likelihood, whereas latent-variable models map data to latent spaces and reconstruct it through decoders.The review discusses autoregressive models, VAEs, and related probabilistic architectures within these categories.
  • Likelihood-based Models: Normalizing flows transform a simple base distribution into a complex molecular distribution using invertible neural transformations with tractable Jacobian determinants.Invertibility and efficient Jacobian determinant calculation are required for tractable maximum-likelihood training.
  • Energy-based Models: Energy-based models assign energies to molecular configurations, but their partition functions are generally intractable and require approximate inference such as contrastive divergence or MCMC.GNNs can parameterize the energy function to capture structural dependencies in molecular graphs.
  • Autoregressive Models: Autoregressive graph models factorize probabilities conditionally and construct molecules step by step by adding atoms, bonds, or substructures.Each action is conditioned on the existing partial graph, enabling sequential sample generation.
  • Diffusion Extensions: Flow-matching extensions use larger integration steps during sampling, significantly reducing computational cost without sacrificing sample quality.The formulation uses a velocity field to govern sample evolution over time.

2.5 Graph Editing and Reinforcement Learning

Graph editing frames molecular generation as sequential decision-making: reinforcement-learning agents modify atoms, bonds, or fragments to optimize predefined objectives. Policy-, value-, and search-based methods provide different mechanisms for selecting edits while trading off stability, data efficiency, and exploration.

  • Graph Editing: Graph editing constructs molecules through sequential addition, deletion, or substitution of atoms and bonds, with edits potentially affecting long-range properties such as synthesizability or bioactivity.This naturally aligns molecular optimization with sequential decision-making.
  • Reinforcement Learning: Reinforcement-learning agents observe a partial molecular graph and select editing actions to maximize cumulative reward under predefined design objectives.Actions can include attaching functional groups or forming bonds.
  • RL Strategies: Policy-based methods optimize action-selection policies, value-based methods estimate future rewards for state-action pairs, and MCTS explores action sequences through tree search.MCTS balances exploration and exploitation through repeated selection, expansion, simulation, and backpropagation.
  • Policy Gradient: Policy-gradient models can fine-tune pretrained molecular policies from general chemical plausibility toward objectives such as improving binding affinity or reducing toxicity.The policy constructs molecules atom by atom or fragment by fragment.
  • Policy Gradient: Positive-reward trajectories become more likely under policy-gradient updates, allowing molecular generation to move toward target-property optimization while maintaining chemical plausibility.Low- or negative-reward trajectories are correspondingly suppressed.
  • PPO: PPO is presented as balancing stability and sample efficiency, supporting efficient exploration while preserving chemical validity in large and complex chemical spaces.The review describes it as widely adopted for molecular generation-policy fine-tuning.
  • On-policy and Off-policy Learning: Off-policy learning improves data utilization by reusing trajectories from another policy, but large policy divergence can produce high-variance importance weights and destabilize training.This distribution-shift issue is an explicit limitation of the approach.
  • Policy Optimization: TRPO constrains policy updates with KL divergence, whereas PPO relaxes this constraint using a KL penalty or clipped objective to improve optimization efficiency.Hard KL constraints incur substantial computational overhead, motivating PPO's alternative formulation.

3. Molecular Property Prediction

GNNs provide a data-driven alternative to handcrafted molecular descriptors by learning task-specific representations directly from molecular graphs. In molecular property prediction, advances in chemical priors, geometric information, multitask learning, pretraining, uncertainty quantification, and explainability address performance, data sparsity, reliability, and interpretability challenges.

  • Foundations: GNNs learn task-specific molecular representations directly from graph structures, avoiding manual feature engineering and its associated biases.This data-driven representation learning has increased interest in GNN-based molecular property prediction.
  • Data-efficient learning: Pretraining, transfer learning, and multitask learning improve performance under limited labels, but task conflict can cause negative transfer.Uncertainty-based task weighting outperformed naive averaging, and auxiliary tasks are not uniformly beneficial.
  • Representation enhancements: Chemical priors and fragment-based representations enrich molecular encoding for property prediction, while geometric GNNs add conformational information for 2D and 3D tasks.The reviewed approaches integrate chemical knowledge, molecular fragments, and spatial encodings into GNN architectures.
  • Uncertainty quantification: Uncertainty-aware methods support more trustworthy property prediction, with pretrained models generally combining higher accuracy and more reliable uncertainty estimates than standard GNNs.Uncertainty estimates also improved hit rates in antibiotic QSAR by prioritizing high-confidence predictions.
  • Interpretability and challenges: Explainability methods trade implementation flexibility against faithfulness and technical difficulty, while causal explanations remain an open direction for improving robustness and generalizability.Post hoc methods are flexible, self-explanation methods more faithfully reflect model reasoning, and future approaches are expected to address causal relationships and learned biases.

4. Virtual Screening

Virtual screening with GNNs spans binding-site identification, docking, ranking, screening, and affinity scoring, with models increasingly incorporating protein structure, geometric information, and interaction-aware learning. The reviewed methods report gains from multi-pose generation, scoring-based selection, equivariant modeling, and joint task training.

  • Binding-site prediction: Binding-site identification starts virtual screening, but remains challenging because templates have limited coverage and sequence models often omit 3D structure.GNNs instead operate directly on protein conformations; GPSite generalizes to low-confidence predicted structures.
  • GNN-based screening tasks: Protein-ligand GNNs model residues, atoms, coordinates, and interactions for binding-site prediction, docking, affinity estimation, and pose evaluation.These tasks include binary binding-site classification, coordinate refinement, and simultaneous pose-affinity prediction.
  • Flexible and generative docking: Generative and structure-aware models address flexible docking and can discover binding modes inaccessible to conventional docking.The review describes RF-AA as combining all-atom precision with biomolecular generalization, while AF3 extends modeling beyond classical docking.
  • Docking and scoring: Multi-pose generation with scoring-based selection consistently outperforms single-shot regression models in accuracy.The reviewed approaches use generated pose ensembles followed by scoring-based selection rather than predicting one pose directly.
  • Affinity prediction: Interaction-aware and equivariant designs improve affinity prediction, with GIGN reducing RMSE from 1.460 to 1.380 and then to 1.297 after force correction.The reported improvements involve separating covalent and noncovalent interactions, comparing invariant and coordinate-based models, and adding pairwise force correction.

3. Multi-task Learning

Multi-task learning combines affinity, pose, distance, and related objectives to improve the breadth of protein-ligand modeling. However, overfitting and dataset bias can cause GNNs to memorize ligand or scaffold associations rather than learn generalizable interaction mechanisms.

  • Multi-task benefits: Coupling scoring, docking, ranking, and screening improves docking and ranking, while data augmentation contributes to screening capability.GenScore balances all four capabilities, and PIGNet2 achieves comparable performance across them.
  • Multi-task objectives: Multi-task models jointly predict affinity, RMSD, ligand conformation, or protein-ligand distance matrices to exploit complementary supervision.PLANET restricts high-RMSD negative samples to affinity prediction, while IGModel jointly predicts affinity and RMSD for docked conformations.
  • Dataset bias: Affinity datasets contain artificial, domain, and label biases that can impair generalization to intermediate activities and novel chemical scaffolds.Extreme activity labels, uneven scaffold representation, and noisy or imbalanced labels are identified as major sources of bias.
  • Overfitting and shortcut learning: Ligand-only or protein-only GNNs can outperform complex-graph models, while a kNN ligand-similarity baseline can match state-of-the-art GNNs.These findings support concerns that models may rely on ligand memorization instead of learning protein-ligand interaction mechanisms.
  • Overfitting and shortcut learning: Explainability analysis attributed approximately 60% of predictive signal to ligand features, 20% to protein features, and 20% to interactions.The review presents this distribution as evidence that current GNNs may memorize ligand-specific features rather than robust interaction patterns.
  • Mitigation strategies: Negative sampling, large-scale pretraining, composite interaction representations, and physically informed objectives are proposed to improve generalization.PIGNet broadens negative samples, Uni-Mol pretrains on billions of molecules and 180,000 protein conformations, and LGN combines CFP, SIFP, and ECIF.

5. Molecular Generation

Molecular generation and optimization can be viewed within a common GNN-based framework, but chemistry imposes validity, structural, target-specific, and synthesis constraints. Generative models therefore aim not only to explore vast chemical space but also to satisfy practical drug-discovery requirements.

  • Chemical-space exploration: Generative approaches are motivated by chemical space estimated above 10^60, compared with databases covering about 10^11 compounds.The review associates this disparity with opportunities to discover unexplored structures having favorable pharmacological and pharmacokinetic properties.
  • Chemical validity: Chemical graph generation is unusually constrained because small changes to atoms, bonds, or bond types can produce invalid molecules.These validity requirements distinguish molecular generation from less chemically structured domains such as image generation.
  • Application-specific constraints: Drug-discovery generation may condition on binding sites, capture 3D conformations, assemble fragments, optimize pharmacokinetic properties, or preserve scaffolds.The review lists structure-based, 3D, fragment-based, property-driven, and scaffold-constrained generation as distinct settings.
  • Unified perspective: Molecular generation and optimization share overlapping formulations and can be unified under a common modeling framework.The review uses this connection to organize GNN-based generative models across drug-discovery scenarios.

5.1 Free Molecular Generation: The Foundation

Free molecular generation models learn distributions over molecular graphs using one-shot or autoregressive strategies, with flow, VAE, GAN, and diffusion approaches addressing variable-size graph generation. One-shot methods are efficient, whereas autoregressive methods generally produce more chemically plausible and structured molecules but require slower sequential inference.

  • One-shot and autoregressive generation: One-shot models generate graph structures in a single step, while autoregressive models construct molecules sequentially.Representative one-shot approaches include GNF, GraphNVP, GraphVAE, MolGAN, GDSS, and DiGress; autoregressive models include MolecularRNN and related methods.
  • Graph-generation formulation: Free molecular generation models learn probability distributions over valid molecular graphs represented by atom features and bond-type adjacency tensors.The graph representation includes variable numbers of atoms, making the dimensions of node and adjacency data depend on n.
  • One-shot generation: One-shot generation improves efficiency but faces variable-size, graph-matching, memory, and chemical-validity challenges.Placeholder atoms support variable sizes but can be memory-inefficient and are generally limited to small-molecule datasets; explicit alignment may also be required.
  • GAN-based generation: MolGAN avoids explicit graph alignment, making generation significantly faster in practice, but GAN training remains unstable because of mode collapse.MolGAN adds reinforcement-learning feedback for desirable chemical properties, while later work explores retraining for stability.
  • Model trade-offs: Autoregressive models generally produce more valid and structured molecules, whereas one-shot methods are more efficient but often less chemically robust.The review identifies a central trade-off between sequential chemical plausibility and parallel generation efficiency.
  • Fragment-based generation: Fragment-based generation restricts construction to synthetically feasible fragments, improving molecular validity and synthesizability.The approach can also generate synthesis routes, linking molecular design more directly to practical synthesis planning.

5.2 Constrained Molecular Generation: Various Application Domains

Constrained molecular generation extends unconditional generation by conditioning on biochemical goals, structural context, or physical priors. The section covers 3D conformation and molecular generation methods, their architectures, and practical limitations.

  • Conditional generation: Conditional generation incorporates constraints such as target proteins, binding pockets, or pharmacophoric features to support goal-directed drug design.This extends unconditional models, which typically generate valid molecules without controlling biochemical objectives.
  • Geometric modeling: Equivariant architectures and physically informed losses preserve symmetry or improve geometric fidelity when generating molecular coordinates.Examples include EGNN-based coordinate generation and alignment-based losses using optimal rigid transformations.
  • 3D conformation generation: 3D conformation generation assumes a molecular graph is given and predicts a physically plausible atomic arrangement by modeling p(R|G).Methods commonly operate through distance matrices or Cartesian coordinates in fixed-dimensional space.
  • 3D conformation generation: Distance-based methods offer flexible edge-wise modeling but may produce reconstruction ambiguity because distance matrices do not uniquely determine Cartesian embeddings.GraphDG predicts pairwise distances from variational latent node embeddings before recovering 3D structures.
  • 3D molecular generation: 3D molecular generation jointly predicts molecular topology and atomic coordinates using one-shot or autoregressive strategies.Coordinates may be represented internally through bond lengths, angles, and torsions, or directly in Cartesian form.
  • 3D molecular generation: One-shot models such as E-NF and EDM add equivariant coordinate generation to molecular modeling but infer bond connectivity post hoc, potentially causing inconsistencies.The review identifies this explicit graph-generation omission as a practical limitation.
  • Practical advances: Physical priors and protein–ligand modeling improve generated-molecule stability, structural diversity, and physical plausibility in reported applications.Reduced RMSD in pre- and post-docking comparisons indicates improved stability, while binding-pocket generation supports structure-based design.
  • Evaluation: CBGBench standardizes comparisons by reimplementing existing structure-based molecular generation methods and evaluating them on a shared CrossDock-based benchmark.The benchmark is intended to support community-wide comparisons under standardized conditions.

5.3 Molecular Optimization: Generate Better Molecules

Molecular optimization modifies existing molecules to improve target properties while preserving chemical validity. The section organizes approaches into search-based, embedding-based, and reinforcement-learning methods, then describes their respective strategies and challenges.

  • Problem formulation: Molecular optimization can be formulated as conditional generation of G_f from G_0 such that h(G_f) > h(G_0).Here h(·) denotes the target property function to maximize.
  • Method categories: The review divides molecular optimization into search-based, embedding-based, and reinforcement-learning approaches.These categories differ in whether they optimize directly over graphs, in continuous latent space, or through learned decision policies.
  • Search-based methods: Search-based methods explore discrete molecular graphs through edits, with genetic algorithms and Monte Carlo tree search as prominent paradigms.Graph edits can include atom addition, ring formation, bond changes, fragment substitution, and rearrangement.
  • Challenges: Discrete graph structure makes gradient-based or classical continuous optimization difficult, motivating graph editing and latent-space alternatives.The review identifies this discreteness as a central challenge for search-based molecular optimization.
  • Search-based methods: GraphGA and related genetic methods combine mutation, crossover, chemical priors, and fragment operations to navigate molecular space toward property improvement.MEGA preferentially uses fragments annotated with synthetic sites and weights, while GraphGA uses subgraph exchange and database-derived priors.
  • Search-based methods: MolSearch uses chemically valid MMP transformations, separates affinity-focused HIT-MCTS from drug-likeness-focused LEAD-MCTS, and applies Pareto ranking across objectives.The two-stage design mirrors lead identification followed by lead optimization.
  • Embedding-based methods: Embedding-based methods encode molecular graphs into continuous latent spaces, optimize latent points, and decode them back into molecular graphs.GraphVAE introduced this strategy to avoid direct optimization over discrete graph structures.

6. Knowledge Graph

Knowledge graphs complement molecular-structure modeling by representing heterogeneous relationships across biological and clinical entities. The section describes their construction and applications to target identification, interaction prediction, adverse-effect estimation, and repurposing.

  • Motivation: Molecular-only models can miss system-level biology spanning genes, proteins, pathways, tissues, phenotypes, and clinical processes.Knowledge graphs address this broader context by integrating heterogeneous information across molecular, cellular, and clinical levels.
  • Knowledge-graph structure: A biomedical knowledge graph represents entities and typed relationships, including genes, proteins, drugs, diseases, pathways, trials, and patient records.Edges are commonly expressed as head–relation–tail triples and may be directed or undirected.
  • Knowledge-graph structure: Knowledge graphs provide greater semantic richness and topological diversity than molecular graphs describing chemical structures.They can encode relations such as gene–protein, drug–protein, and mutation–disease associations.
  • Data sources: Construction integrates resources such as NCBI Gene, Ensembl, TCGA, UniProtKB, BioGRID, and IntAct for genomic, clinical, protein, and interaction information.These resources support entity alignment, functional annotation, multi-omics integration, and protein-interaction modeling.
  • Drug-discovery applications: Knowledge graphs can support mechanistic interpretation, drug-repurposing discovery, and analysis of disease-relevant protein mechanisms.They also enable automated reasoning, semantic querying, and graph-based inference over integrated biomedical contexts.
  • Drug-discovery applications: GNN-based knowledge-graph systems support target identification, drug-interaction prediction, adverse-effect estimation, and drug repurposing.The review emphasizes tailored GNN architectures for integrating heterogeneous information and validating or interpreting biomedical predictions.
  • Clinical context: Drug-combination effects can vary across cell lines, with approximately one-third of combinations showing opposite effects in different cellular contexts.This motivates phenotype-level and patient-aware modeling for clinical applicability.
  • Practical considerations: Reliable knowledge-graph prediction requires complete infrastructure, data curation, provenance tracking, and robustness-oriented engineering rather than a single model alone.The review identifies infrastructure design and future language-model-assisted curation as important directions.

7. Chemical Synthesis

Chemical synthesis is central to translating computational designs into experimentally testable molecules, motivating computer-aided synthesis planning and data-driven GNN methods. This section covers reaction-data representation, dataset characteristics, reaction prediction, and emerging directions for synthesis-aware molecular design.

  • Organic synthesis forms the Make stage of the Design-Make-Test-Analyze cycle, and synthetic accessibility influences the efficiency and cost of experimental validation.
  • Reaction data representation: Reaction transformation data encode reactants, reagents, and products with reaction SMILES, while atom mapping supports reaction-center identification and template extraction.Reaction SMARTS and SMIRKS specify reaction-center environments including valence, bond order, formal charge, and local structure.
  • Reaction data limitations: Most public reaction datasets omit byproducts, producing incomplete atom mapping for leaving groups and auxiliary fragments.
  • Reaction datasets: High-throughput experimentation datasets are more standardized and less noisy than general-purpose datasets but cover a narrower chemical space.This trade-off limits their utility for large-scale synthesis planning.
  • Synthesis planning models: Localized-template methods reduce synthesis-search complexity: LocalTransform uses ~100 generic templates to cover 94.6% of training-set reactions.WLDN introduced localized templates, while LocalTransform further optimized local template construction.
  • Reaction representation learning: Reaction fingerprints outperform rule-based and language-model representations on reaction classification without task-specific fine-tuning, indicating transferability of self-supervised representations.
  • Reaction property prediction: Prediction quality remains constrained on heterogeneous reaction data: R² values rarely exceed 0.2, and the best Reaxys models reach around 65 ± 5% accuracy for yields above 65%.

8. Conclusions and Perspectives

The review concludes that GNNs can evolve from molecular predictors into relational integrators connecting chemical structures with biological outcomes. Progress depends on addressing data, validity, generalization, and multi-objective challenges while incorporating richer structural, physical, and expert information.

  • The review covers GNN applications spanning molecular property prediction, virtual screening, reaction modeling, molecular generation, and biomedical knowledge graph construction.
  • Challenges: Recurring obstacles include data sparsity, physical validity, overfitting, metric bias, and tension among multiple design objectives.
  • Perspectives: GNNs’ broader potential lies in connecting chemical structures to biological modalities governing therapeutic response, beyond learning representations from individual molecular graphs.
  • Perspectives: The review envisions message-passing schemes extending beyond intramolecular neighborhoods to protein interfaces and GNNs linking molecular design decisions with biological outcomes.The stated outcome is accelerating the rational discovery of safe, efficacious therapeutics.
  • Methodological directions: Future architectures are described as incorporating symmetry awareness, differentiable physical constraints, foundation-scale pretraining, causal explanation, calibrated uncertainty, and expert feedback.

Biographies

The biographies describe a research team spanning computational chemistry, molecular simulation, graph learning, generative AI, synthesis planning, and AI-driven drug discovery. Their work covers molecular design, biological aging, protein–ligand modeling, and autonomous synthesis planning.

  • Generative and multiscale modeling: Other research areas include general-purpose molecular design, small-molecule and protein generation, molecular simulation, and multiscale biomolecular design.
  • Synthesis planning: Several contributors focus on synthesis planning, including algorithmic drug synthesis planning, hybrid organic–enzymatic synthesis, and natural-language technologies for autonomous planning.
  • The contributors’ research spans AI-driven molecular property prediction, molecular optimization, interpretable machine learning, molecular design, and computer-aided drug discovery.
  • Molecular and biological modeling: The team includes expertise in graph representation learning for therapeutic-target discovery, structure-based virtual screening, protein–ligand interaction prediction, and molecular docking.
  • Adjacent research areas: One contributor studies aging systems biology using causal inference and biological-age data resources, while another works at the intersection of computational theory, algorithms, and drug discovery.
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