The research team, led by Andrew Disharoon of the Medical University of South Carolina and Aaron J. Masino of Clemson University, assembled a dataset of 377,628 drug-pair samples drawn from DrugBank and the RxNav Drug and Drug Interaction dataset, with chemical structures retrieved through the PubChem API and therapeutic target information from the Therapeutic Target Database. Rather than relying on a small set of broad interaction categories, the team processed the free-text interaction descriptions in these databases—for example, sentences like “the metabolism of (R)-warfarin can be increased when combined with acetaminophen”—and extracted 168 normalized interaction templates by masking out the drug names. Each sample therefore consists of a drug pair, structural and target information for both compounds, and a set of binary labels indicating which of the 168 interaction types apply. Careful preprocessing removed formulation-specific terms such as “hydrochloride” or “maleate” so that, for instance, metoprolol succinate and metoprolol tartrate are treated as the same base drug, and a synonym list unified names like acetaminophen and paracetamol. Interactions with fewer than 50 samples were dropped to ensure enough data for reliable training.

A central design choice distinguishes InfoMedex from earlier approaches: the way molecules are turned into sequences a neural network can read. Instead of treating SMILES strings—compact text encodings of molecular structure—as ordinary character strings, the team converted them to Atom-in-SMILES notation, in which each token explicitly encodes an atom together with its local bonding environment, including ring membership, aromaticity, and attached hydrogens. This chemically meaningful tokenization preserves structural context that character-level splitting or compressed molecular fingerprints can lose, and it is particularly well suited to transformer architectures, where positional embeddings and self-attention can capture relationships between distant substructures. Prior work has shown that Atom-in-SMILES representations better capture local chemical context than character-level tokenization, facilitating substructure-level reasoning.

The architecture itself is a hybrid of transformer and convolutional components processing three input streams. Tokenized Atom-in-SMILES sequences for the drug of interest and for the interacting drug are each converted into dense embeddings of dimension 512 with learned positional encodings. Gene features derived from the Therapeutic Target Database, one-hot encoded across 1,143 features, pass through a two-layer feed-forward network into the same embedding space and are replicated across the sequence length. The three streams are concatenated into a unified sequence of size 3,072 and processed by a transformer encoder with eight attention heads, pre-layer normalization, and padding masks to keep padding tokens out of attention computations. A convolutional stack of four 1D layers with 124 filters and kernel size 7, ReLU activations, a dropout layer, and a linear classification head then produces logits for all 168 interaction labels. The full model contains roughly 70.35 million learnable parameters and was trained with sigmoid focal loss, an objective designed for severe class imbalance, reaching peak validation performance after an average of 22.8 epochs.

The performance results are striking, though they come with an important caveat. Under the primary evaluation, in which drug pairs are split randomly but individual drugs may appear in both training and test sets, the model achieved a micro-averaged area under the precision-recall curve of 0.857 and a macro mean AUPRC of 0.825 across the 168 labels. Accuracy averaged 0.997 and AUC-ROC 0.998, while precision-recall analysis showed the model maintaining precision above 70 percent at recall values as high as 80 percent. Given that only 0.603 percent of the more than 6.3 million test predictions were positive labels, the researchers deliberately relied on precision-recall curves rather than ROC curves, which prior studies have shown to be essentially uninformative under extreme class imbalance. Stratified analysis across frequency quantiles—from interactions positive in as few as 0.013 percent of samples up to the most common at 13.4 percent—showed median precision above 0.8 across all quantiles. For three very rare interaction types the model’s predicted probabilities failed to exceed the 0.5 decision threshold, but the probability distributions for positive and negative samples were largely separated, suggesting appropriate model uncertainty rather than outright failure.

That caveat emerged from a secondary, more demanding evaluation. When the team held out 10 percent of the drugs—86 compounds appearing as the drug of interest in at least one sample—and excluded every sample containing them, mean AUPRC fell from 0.825 to 0.341 across 142 evaluable labels. In other words, InfoMedex generalizes well to unseen drug pairs involving drugs it has encountered elsewhere in training, but predicting interactions for entirely unseen molecules remains substantially harder. This distinction matters enormously for the model’s ultimate use case: flagging risks for newly approved drugs that have no interaction history at all. The authors are candid that this limitation should temper expectations for near-term deployment on novel compounds.

Perhaps the most intriguing contribution is the explainability analysis. Using Integrated Gradients, a gradient-based attribution method implemented in the Captum library, the team computed the contribution of each atom token to selected predictions, aggregating per-sample ranks across many partner drugs to obtain a robust estimate of which molecular regions matter most. Because a given drug’s token sequence and input position are fixed across samples, the averaging marginalizes only over the partner drug, yielding a well-defined estimand: the typical within-drug importance ranking of each token over the empirical distribution of partners. Rank aggregation was chosen over magnitude-based averaging because it is robust to samples carrying disproportionate attribution mass, and interquartile ranges were reported to measure rank stability.

The case studies focused on two well-characterized cytochrome P450 inhibitors. For bupropion, a CYP2D6 inhibitor represented by 29 atom tokens, the highest-importance tokens clustered on the carbonyl-centered motif—the ketone carbonyl carbon had a mean rank of 1.88—and on the bulky N-alkyl tert-butylamine region, including the quaternary carbon at mean rank 6.42 and the basic nitrogen. Both patterns align with known pharmacology: the carbonyl is the starting point for reductive metabolites that show stronger CYP2D6 inhibition than the parent drug, and CYP2D6 substrates and inhibitors frequently present a basic nitrogen that contributes to binding in the enzyme’s tight active site. The meta-chloro phenyl region also ranked prominently, consistent with evidence that this motif discourages aromatic hydroxylation and redirects metabolism toward CYP2B6, which produces hydroxybupropion—the metabolite responsible for roughly two-thirds of bupropion’s in vivo CYP2D6-inhibiting activity.

Ritonavir, the HIV protease inhibitor deployed clinically precisely because of its potent CYP3A4 inhibition, presented a different picture. Represented by 83 atom tokens with 18 points of rotation, the molecule drew the model’s strongest emphasis to the central scaffold, where the hydroxyl token had the highest mean rank of 1.39 with a narrow interquartile range of 1.0 to 1.75 across 18 partner drugs, followed by an adjacent stereocenter. These atoms sit near the benzyl and phenyl bulk that provides steric stabilization, helping hold the drug in a binding-ready configuration within the CYP3A4 cavity. Structural studies have shown ritonavir is a type II ligand that coordinates the heme iron through a thiazole nitrogen while packing hydrophobic groups tightly into the active site, effectively locking the molecule into a high-affinity pose with a slow off-rate. The model also emphasized carbonyl-linked oxygen tokens and the methylene linker connecting the heteroaryl ring to the tertiary amine arm, features that plausibly govern how the bulky aromatic half and the heme-facing heteroaryl half engage the enzyme. Notably, some highly ranked tokens were non-atom structural signposts such as parentheses, which the authors suggest the model uses to guide its structural reading of the molecule rather than as chemically interpretable features.

Taken together, the results position InfoMedex as a scalable, interpretable complement to post-marketing surveillance and formal interaction studies, of which only a limited subset of clinically relevant combinations can feasibly be evaluated under FDA guidance. The ablation finding that randomly perturbing the target features did not significantly change performance metrics suggests the current one-hot gene encodings underexploit mechanistic context, and the authors propose graph neural networks or cross-attention architectures as avenues to better fuse drug-target data. The held-out-drug results temper enthusiasm for novel compounds, and the explainability analysis covered only interactions with well-studied CYP-mediated mechanisms. Still, the demonstration that a transformer reading molecules atom by atom can both predict 168 interaction types with high precision and point to the specific chemical motifs responsible marks a meaningful step toward AI systems that do not just flag drug interactions but can generate mechanistic hypotheses about why they occur—exactly the kind of transparent reasoning that clinical and regulatory adoption will demand.

Subject of Research: Deep learning prediction and explainability of drug-drug interactions using Atom-in-SMILES tokenization and a multimodal CNN-transformer architecture

Article Title: InfoMedex: drug-drug interaction prediction via a multimodal CNN-transformer model

Article References: Disharoon, A., Pasupuleti, S., Thurston, C., Fiore, M., McKenzie, S., Smith, A., & Masino, A. J. (2026). InfoMedex: drug-drug interaction prediction via a multimodal CNN-transformer model. Frontiers in Drug Safety and Regulation, 6, Article 1841035. https://doi.org/10.3389/fdsfr.2026.1841035

Image Credits: AI Generated

DOI: 10.3389/fdsfr.2026.1841035

Keywords: drug-drug interactions, deep learning, transformer, Atom-in-SMILES, explainable AI, Integrated Gradients, cytochrome P450, pharmacovigilance, DrugBank, bupropion, ritonavir, precision medicine

The core problem the researchers set out to solve lies in how existing algorithms represent drug molecules. Most current methods treat a molecule as a static picture of atoms and bonds, which means they often miss how substructures relate to each other dynamically or how the semantic meaning of one functional group changes the interpretation of another. In a drug interaction context, those overlooked relationships can be exactly the ones that matter, because the biological effect of a compound frequently emerges from the interplay between its chemical components rather than from any single fragment.

MEGH-DDI attacks this limitation with a multimodal design that pairs two complementary ways of understanding a molecule. The first is a dual-channel semantic module that combines Mol2vec, a technique that converts molecular substructures into numerical vectors, with MolT5, a chemical language model adapted from the text-processing architectures that power modern natural language systems. By running both channels in parallel, the framework captures fine-grained substructure information alongside broader chemical semantics, much as a reader might combine knowledge of individual words with an understanding of the sentence they appear in.

Because merging two representation channels can introduce redundant or noisy features, the team added a dynamic feature selection step based on SHAP, a widely used interpretability technique that estimates how much each feature contributes to a prediction. Features that add little signal are down-weighted or removed, which keeps the model focused on the molecular details that genuinely drive interactions. This pruning step is not merely cosmetic; by reducing redundancy it helps the downstream network learn cleaner patterns and improves the model’s ability to generalize beyond the drugs it has already seen.

The second pillar of the framework is structural. An edge-aware graph convolutional network, built on the GCNII architecture, treats each molecule as a graph in which atoms are nodes and chemical bonds are edges. What makes this version distinctive is its bond-level attention, which allows the network to distinguish between different types of chemical bonds rather than treating every connection identically. A single bond and an aromatic bond carry very different chemical implications, and the attention mechanism lets the model weigh those differences explicitly as information propagates through the molecular graph.

Perhaps the most inventive component is the Hop2Token module. Graph neural networks typically aggregate information from immediate neighbors, but important chemical relationships can span several bonds. Hop2Token converts multi-hop neighborhoods, meaning the atoms reachable within two, three or more steps across the molecular graph, into token sequences. These sequences can then be processed by a Transformer, the same architecture underlying modern large language models, allowing the framework to model high-order dependencies between distant substructures that conventional message-passing networks would struggle to capture.

The performance numbers reported in the study are striking. On the widely used DrugBank dataset, MEGH-DDI reached an accuracy of 98.36 percent in the transductive setting, where the model is evaluated on drug pairs it has encountered during training. Both the area under the ROC curve and average precision exceeded 99.7 percent, placing the framework ahead of earlier baselines and recent state-of-the-art methods. On the ChChMiner dataset, the model achieved an F1 score of 99.05 percent, an improvement of 2.74 percentage points over the best previously published approach.

Transductive results alone would be impressive but limited, since real-world drug safety questions usually involve newly approved compounds that no model has seen before. The researchers therefore also tested MEGH-DDI in inductive settings, where interactions involving previously unseen drugs must be predicted. The framework remained competitive across most evaluation metrics on both datasets and outperformed competing methods on several key measures, suggesting that its combination of semantic and structural learning genuinely generalizes rather than memorizing training pairs.

Accuracy alone is rarely enough in a medical context, where clinicians and regulators need to understand why a model flags a particular combination as risky. To address this, MEGH-DDI incorporates a three-level interpretability mechanism that spans atoms, chemical bonds and topological paths through the molecular graph. This design highlights which atoms and bonds contributed most to a given prediction, giving researchers a chemically meaningful explanation rather than an opaque score. Such transparency could make the tool more trustworthy for drug safety assessment and for guiding the design of new compounds with fewer interaction liabilities.

The team has made the framework broadly accessible, releasing code and datasets through a public GitHub repository and offering a free web platform where researchers can run predictions without installing anything. As polypharmacy becomes increasingly common in aging populations and combination therapies grow central to treating complex diseases, tools like MEGH-DDI point toward a future in which dangerous drug interactions can be flagged computationally before they ever reach a patient, accelerating both drug safety evaluation and the search for safer new medicines.

Subject of Research: Prediction of drug–drug interactions using multimodal molecular representation learning with graph neural networks and chemical language models

Article Title: MEGH-DDI: MolT5-driven edge-aware graph learning with Hop2Token representation for drug–drug interaction prediction

Article References: Zhou, L., Zuo, Y., He, W., & Deng, Z. (2026). MEGH-DDI: MolT5-driven edge-aware graph learning with Hop2Token representation for drug–drug interaction prediction. PLOS Computational Biology, 22(10), e1014393. https://doi.org/10.1371/journal.pcbi.1014393

Image Credits: AI Generated

DOI: 10.1371/journal.pcbi.1014393

Keywords: drug–drug interactions, MEGH-DDI, MolT5, graph neural networks, Transformer, molecular representation, pharmacology, drug safety, DrugBank, ChChMiner, SHAP interpretability, computational biology

Drug-target interaction prediction sits at the heart of modern drug repurposing and target identification. When a protein emerges as a promising therapeutic target, perhaps because of new genetic evidence linking it to disease, researchers urgently need to know which existing or candidate compounds might bind to it. Experimentally screening every possible compound against every possible target is prohibitively expensive, so computational models step in to prioritize the most likely interactions. The difficulty is that the most clinically interesting cases are precisely the ones where data is scarcest. A protein with no known ligands offers nothing for a model trained on interaction labels to latch onto, forcing the system to rely entirely on intrinsic properties: the three-dimensional arrangement of atoms in a drug molecule, the amino acid sequence of a protein, and the structural patterns that connect molecular architecture to binding behavior.

The central insight behind HierHGT-DTI is that biological entities naturally exist at multiple scales simultaneously, and that most existing models fail to exploit this hierarchy coherently. A drug molecule can be described at the level of individual atoms, at the level of functional substructures such as rings and side chains, and at the level of the whole compound. A protein can be described residue by residue, through sequence-derived communities of residues that capture intermediate-scale organization, and as a complete macromolecule. Rather than encoding each scale separately and hoping a downstream classifier stitches them together, the new framework represents each candidate drug-protein pair as a bilateral multiscale graph. This graph contains atom nodes, substructure nodes, whole-drug nodes, residue nodes, residue-community nodes, and whole-protein nodes, all connected within one typed structure that spans the fine details of chemistry and the broad sweep of protein organization.

On top of this multiscale scaffold sits a heterogeneous graph transformer, a neural architecture built to reason over graphs whose nodes come in different types and whose edges carry different meanings. The transformer coordinates several distinct kinds of relations within the graph: local relations that connect neighboring entities at the same scale, adjacent-scale relations that link atoms to substructures and residues to residue communities, direct fine-to-global relations that jump from individual atoms all the way to whole proteins, and drug-protein relations that bridge the two sides of the pair. This relation-aware attention mechanism allows the model to learn which pathways of evidence matter most for a given prediction, effectively letting an atom-level chemical detail inform a protein-level judgment through a chain of typed, learnable connections rather than through hand-engineered features.

To assess how well this design works, the researchers benchmarked HierHGT-DTI against six matched baseline models across three widely used drug-target interaction datasets: DrugBank, BioSNAP and BindingDB. Crucially, the evaluation was repeated across five random seeds, a practice that guards against the possibility that a single favorable data split produced flattering results. The evaluation settings included random splits, where training and test interactions are drawn from the same distribution, as well as cold-drug and cold-protein splits, where entire molecules or proteins are held out of training entirely. The cold-protein setting is widely regarded as the most demanding and the most clinically relevant, because it directly simulates the scenario of a newly implicated target arriving with no ligand history.

The results were striking. HierHGT-DTI ranked first in all six DrugBank and BioSNAP evaluation settings, covering both random and cold-start conditions on both datasets. The largest margins appeared exactly where the problem is hardest: under cold-protein evaluation, the model’s AUROC, a measure of how well the model separates true interactions from non-interactions across all classification thresholds, improved by 8.8 percentage points on DrugBank and 5.8 percentage points on BioSNAP compared with the strongest baseline. In a field where models often compete over fractions of a percentage point, gains of this magnitude under cold-start conditions represent a substantial advance, and they suggest that the multiscale, relation-aware architecture is genuinely capturing transferable structure rather than memorizing interaction patterns.

The picture on BindingDB was more nuanced, and this honesty about mixed results adds credibility to the overall claims. BindingDB differs from the other two benchmarks in that it focuses on quantitative binding affinities rather than simple binary interactions, and its chemical space is broader and more skewed. On this dataset, HierHGT-DTI achieved the highest AUPR, or area under the precision-recall curve, in the cold-protein setting, a metric that is particularly informative when positive interactions are rare and the cost of false leads is high. However, the baseline model HiGraphDTI led the random and cold-drug evaluations on BindingDB. This pattern indicates that the new framework’s advantage is concentrated precisely in the cold-protein regime, which the authors identify as the setting most relevant for prioritizing candidate interactions involving previously unseen protein targets.

Why should coordinating multiple scales matter so much for cold-start generalization? The likely explanation lies in how information flows when interaction labels are absent. A model that only sees whole-molecule representations must somehow compress all binding-relevant chemistry into a single vector, and a model that only sees atoms must rediscover high-level patterns from scratch for every new protein. By building explicit pathways from atoms through substructures to whole drugs, and from residues through residue communities to whole proteins, HierHGT-DTI gives the transformer a rich menu of abstraction levels to draw from. When a completely new protein appears, the model can still recognize familiar residue-community motifs, recurring substructural chemistry on the drug side, and cross-scale relations that survived training on other targets. In effect, the architecture provides many partial routes to a prediction, so the absence of interaction labels for one specific protein does not sever the entire chain of evidence.

The practical implications extend across several areas of pharmaceutical research. For drug repurposing, a model that performs well on cold proteins can scan established compound libraries against emerging targets from genetic studies, flagging candidates for experimental validation before costly assays begin. For structure-based drug design, the multiscale representation offers a way to connect atomic-level binding determinants with protein-level function. The authors also note that the framework is relevant to phenotypic drug screening and molecular target identification, both of which depend on reliably linking compounds to the proteins through which they act. Because the model transfers structural and sequence evidence rather than relying on interaction history, it is naturally suited to poorly characterized proteins, including those produced by understudied genes that the scientific community has only recently begun to annotate.

Transparency accompanies the methodological advance. The study was supported by the Hunan Provincial Natural Science Foundation Key Joint Project Between Province and City, and the authors declare no competing interests. Importantly for the research community, the source code, processed data splits and configuration files are publicly available, allowing other groups to reproduce the benchmarks, apply the framework to their own targets, and build on the multiscale graph design. Published open access in BMC Bioinformatics, the work arrives at a moment when machine learning is reshaping how the earliest stages of drug discovery are conducted. If the cold-start gains reported here hold up in real-world target discovery pipelines, the era in which a newly implicated protein sat beyond the reach of computational prediction may be drawing to a close, replaced by graph-based models that read biology at every scale at once.

Subject of Research: Cold-start drug-target interaction prediction using a multiscale relation-aware heterogeneous graph transformer

Article Title: HierHGT-DTI: a multiscale relation-aware heterogeneous graph transformer for cold-start drug-target interaction prediction

Article References: Chen, Z., & Wan, Y. (2026). HierHGT-DTI: a multiscale relation-aware heterogeneous graph transformer for cold-start drug-target interaction prediction. BMC Bioinformatics. https://doi.org/10.1186/s12859-026-06637-3

Image Credits: AI Generated

DOI: 10.1186/s12859-026-06637-3

Keywords: drug-target interaction, cold-start prediction, heterogeneous graph transformer, multiscale representation, machine learning, drug discovery, DrugBank, BioSNAP, BindingDB, protein targets, graph neural networks, bioinformatics