Small molecules face a formidable structural challenge, however. The PD-1/PD-L1 interface is a broad, shallow protein-protein contact surface, long considered a difficult target for drug-like compounds. Progress has come from chemotypes such as the biphenyl-based inhibitors developed by Bristol-Myers Squibb, including BMS-202, which bind PD-L1 and induce dimerization that blocks PD-1 recognition. Compounds such as INCB086550 and the oral modulator CA-170 have entered early clinical evaluation, proving that small-molecule checkpoint inhibition is feasible. Still, only a small fraction of explored scaffolds has advanced to the clinic, and screening the relevant chemical space experimentally is costly and slow. Computational triage has therefore become essential for deciding which candidates deserve synthesis and bioassay follow-up.

A team led by Monsin Sangsawat and Pornchai Rojsitthisak at Chulalongkorn University, with collaborators including Worathat Thitikornpong, Cong Feng, and Ming Chen, has now published an integrated solution in the journal Results in Chemistry. Their study reports an open-source, end-to-end cheminformatics framework for predicting the potency of small-molecule PD-L1 inhibitors, built entirely in the KNIME Analytics Platform. Every processing step, from data retrieval to model deployment, is encoded as an explicit node sequence, and the executable workflow file, curated datasets, and fixed split identifiers have been deposited in a public GitHub repository. The design directly confronts a persistent problem in the field: many published machine-learning models for PD-L1 are difficult to reproduce because training data, preprocessing details, or workflow code are inaccessible, and few are evaluated across independent data sources.

The foundation of the framework is a rigorously curated dataset. The researchers queried ChEMBL, targeting the human PD-L1 entry CHEMBL4523993, and retrieved more than 15,000 bioactivity records. To reduce label noise, they applied assay-level filters restricting the modeling set to homogeneous Alpha and HTRF assay formats, the two readouts with the most consistent annotation and the broadest activity coverage. After removing ambiguous qualifiers, duplicates, and non-numeric annotations, and after converting IC50 values to logarithmic pIC50 units, 2413 unique compounds remained. These were stratified into three potency tiers: active compounds with IC50 below 10 nanomolar, moderate inhibitors between 10 and 100 nanomolar, and weak or inactive compounds above 100 nanomolar. The curated set spans 953 unique Bemis-Murcko scaffolds, preserving the chemical diversity reported for PD-L1 inhibitors, from biphenyls and combretastatin analogs to cyclic peptides.

Molecular representation was treated as a central experimental variable. Each compound was encoded with 119 RDKit physicochemical descriptors, which were filtered for zero variance, pruned for high inter-correlation, and refined using recursive feature elimination with cross-validation, ultimately yielding 23 descriptors covering lipophilicity, ring content, van der Waals surface areas, and molecular quantum numbers. In parallel, two fingerprint families were generated: MACCS structural keys and Morgan circular fingerprints with a radius of two, together with a hybrid concatenation of the two. Four regression algorithms were benchmarked across the three representations, producing twelve configurations: Random Forest, a Keras deep neural network, Gradient Boosting, and XGBoost. Each configuration was trained under four fixed random seeds, and results were reported as means with standard deviations to capture stochastic sensitivity.

The Random Forest consistently delivered the most reliable balance between fit and generalization. Using the hybrid MACCS-Morgan representation, it achieved a coefficient of determination of 0.871 on the held-out test set and 0.863 on a strictly external validation set of 403 compounds that were never touched during model selection. Cross-validated Q2 values ranged from 0.888 to 0.902 across descriptor sets, and external Qext2 values reached as high as 0.919 with MACCS fingerprints alone. The deep neural network, although it attained higher training performance for some representations, showed a larger decline on held-out data, suggesting a greater tendency toward overfitting at the current sample size. Importantly, the team verified that no test or external compound shared an identical canonical SMILES with any training compound, and performance was unchanged after excluding high-similarity nearest neighbors, ruling out information leakage through duplicates.

Generalization was then stress-tested with a scaffold-disjoint partition, in which entire Bemis-Murcko scaffold families were assigned exclusively to training or held-out sets. Under this stricter regime, the hybrid Random Forest model achieved a mean test R2 of 0.858 and external validation R2 of 0.851, closely matching the random-split results. This indicates that the reported accuracy was not inflated by scaffold sharing and that the model genuinely extends to structural families absent from training. Prediction reliability was strongest in the moderate-to-potent activity range most relevant to virtual screening, while weak inhibitors with pIC50 below 7 showed larger errors and a modest tendency toward over-prediction, a pattern attributed to the relative scarcity of weak compounds in the curated data.

A distinctive strength of the study is its emphasis on knowing when to trust the model. Two applicability-domain diagnostics were implemented: a distance-based criterion derived from the mean and standard deviation of minimum training-set distances, and a leverage-based Williams plot with the classical threshold of three times the number of descriptors divided by the number of training compounds. Nearly all test and external-validation compounds fell inside the domain, and out-of-domain compounds in the independent BindingDB application set showed markedly larger errors than in-domain ones, with mean absolute errors of 1.884 versus 0.809 under the distance criterion. The researchers applied the model to 124 unique BindingDB compounds after removing 444 overlapping entries. Compounds sharing biphenyl scaffolds or cyclic peptide chemotypes with the training distribution showed small residuals, while structurally distinct pyrazolone derivatives and arylindanyloxypyridine frameworks showed substantially larger errors, pinpointing precisely where future data expansion would pay off.

The framework also translates statistical features into medicinal chemistry insight. SHAP analysis of the Random Forest identified the number of aromatic heterocycles, molar refractivity, and lipophilicity as the most influential descriptors, with threshold-like dependencies. The strongest potency-associated MACCS key simply records the presence of chlorine, but the corresponding Morgan features resolve it more sharply as a chlorinated aryl ring system and a chloro-substituted biaryl, discriminating potency with correlations up to 0.75. Conversely, a potency-decreasing CH2-O linkage characteristic of dibenzyl ether motifs is captured by MACCS but not the leading Morgan features, explaining why the two fingerprint blocks carry non-redundant, oppositely signed information. Scaffold analysis showed simple dibenzyl-ether and stilbene-like cores averaging pIC50 values of 6.3 to 7.3, while phenylpyridine and phenylpyrazine cores bearing basic amines averaged 8.4 to 10.2. These trends map directly onto the predominantly hydrophobic PD-L1 binding cleft, defined by residues such as Tyr56, Met115, and Tyr123, where halogenated aromatic extension and nitrogen heterocycles favor productive binding, and outlier compounds flagged by the model were examined through GNINA docking at this cleft against the reference inhibitor BMS-202.

The authors are candid about limits. Assay-format annotations come from ChEMBL descriptions rather than independently verified conditions, which may bias the set toward chemotypes preferentially tested in Alpha and HTRF formats, and a comparison against the published PDL1-inhi.predictor showed that an external model captured the BindingDB chemical space somewhat better. Yet the study’s contribution extends beyond any single metric: it delivers a transparent, inspectable, and adaptable platform that couples potency prediction with uncertainty quantification, supports pre-docking prioritization, and can be extended with uncertainty-guided active learning. The team suggests that high-ranking compounds within the applicability domain, especially those representing novel chemotypes, could be selected for prospective testing in the same Alpha or HTRF assays, followed by model retraining. For a field where reproducibility is often the missing ingredient, an open workflow anyone can audit and rerun on an ordinary laptop may prove as valuable as the model itself.

Subject of Research: Machine learning-guided discovery and prioritization of small-molecule PD-L1 inhibitors using an open-source KNIME workflow with curated bioactivity data and applicability-domain assessment

Article Title: Machine-learning-guided discovery and prioritization of PD-L1 inhibitors: An open-source KNIME workflow with curated bioactivity data, molecular fingerprints, and applicability-domain assessment

Article References: Sangsawat, M., Watpuang, B., Nathapinthu, T., Srisiriroj, P., Nalinratana, N., Taechawattananant, P., Thitikornpong, W., Feng, C., Chen, M., & Rojsitthisak, P. (2026). Machine-learning-guided discovery and prioritization of PD-L1 inhibitors: An open-source KNIME workflow with curated bioactivity data, molecular fingerprints, and applicability-domain assessment. Results in Chemistry, 30, Article 103877. https://doi.org/10.1016/j.rechem.2026.103877

Image Credits: AI Generated

DOI: 10.1016/j.rechem.2026.103877

Keywords: PD-L1, PD-1/PD-L1 immune checkpoint, machine learning, KNIME workflow, Random Forest, molecular fingerprints, MACCS, Morgan fingerprints, applicability domain, ChEMBL, BindingDB, cancer immunotherapy

The research, led by Alan Victor de Souza Pinho and Rosalvo Ferreira de Oliveira Neto of the Federal University of San Francisco Valley, together with Edilson Beserra de Alencar Filho, tackles a stubborn bottleneck in environmental risk assessment. Experimental toxicity testing is expensive, slow, and ethically fraught, and for pollinators the available data are especially sparse. Quantitative structure-activity relationship, or QSAR, modeling offers a computational alternative, predicting a compound’s biological effects directly from its molecular structure. But QSAR models are only as good as the molecular representations fed into them, and building those representations traditionally requires laborious feature engineering with specialized descriptor libraries.

The team drew its data from ApisTox, a recently released open-access benchmark containing curated toxicity information for 1,035 compounds, of which 296 are classified as toxic to honey bees and 739 as non-toxic. The classification follows the United States Environmental Protection Agency’s regulatory convention: a compound is deemed toxic if its acute LD50 value, whether by oral or contact exposure, is at or below 11 micrograms per bee. The dataset aggregates information from authoritative sources including the EPA’s ECOTOX knowledgebase and the Pesticide Properties DataBase, and for each chemical it retains the most toxic exposure route, a conservative strategy designed to capture the highest potential risk to pollinators.

Into this benchmark the researchers introduced three competing ways of describing molecules. The first used PaDEL, a widely adopted open-source software that calculates hundreds of handcrafted molecular descriptors. The second used Morgan fingerprints computed with RDKit, circular substructure patterns that encode the atomic neighborhoods of each molecule at fixed radii. The third and most novel approach extracted embeddings from MolFormer, a BERT-style chemical language model developed by IBM that treats SMILES strings, the text-based notation for molecular structures, as a language. MolFormer was pretrained on millions of molecules from the PubChem and ZINC databases using a masked language modeling objective, learning to predict hidden tokens from their context and thereby absorbing structural, semantic, and physicochemical information without any labeled toxicity data.

Crucially, the researchers did not fine-tune the massive model. Instead, they adopted a feature-based transfer learning strategy, using the pretrained network as a frozen feature extractor that converts each SMILES string into a dense 768-dimensional vector. These embeddings then served as input to three classical machine learning classifiers: Random Forest, Support Vector Machine, and a Multilayer Perceptron. All models were evaluated with five-fold cross-validation using the area under the receiver operating characteristic curve, or ROC-AUC, a threshold-independent metric well suited to the dataset’s class imbalance. Notably, the embeddings came from a publicly available reduced-scale MolFormer checkpoint trained on only about 100 million molecules, roughly ten percent of the combined ZINC and PubChem corpora, since the full-scale checkpoints trained on the reported 1.1 billion molecules are not publicly available.

The results delivered a clear verdict. Random Forest paired with Morgan fingerprints achieved the best overall discrimination, with a mean ROC-AUC of 0.866, confirming that substructure-based representations remain formidable baselines. Yet Support Vector Machine combined with MolFormer embeddings came astonishingly close, reaching a ROC-AUC of 0.859, a gap of just 0.007. Considering that the embeddings originated from a comparatively lightweight pretraining regime, the near-parity is striking, and it raises an tantalizing question the authors pose explicitly: had the full-scale model trained on the complete 1.1-billion-molecule corpus been accessible, the remaining gap might have narrowed further or even reversed.

Against the older PaDEL descriptors, the verdict was unambiguous. MolFormer embeddings outperformed PaDEL across all three classifiers, with ROC-AUC gains ranging from 0.005 to 0.021, the largest improvement appearing for the Support Vector Machine. The authors attribute this to the alignment between MolFormer’s learned representations and margin-based decision boundaries. More fundamentally, the advantage reflects a difference in representational philosophy. Morgan fingerprints encode the presence of local substructures without capturing the global molecular context in which a functional group sits, and their binary hashing is vulnerable to bit collisions. MolFormer’s bidirectional self-attention, by contrast, encodes every atomic position in relation to all other tokens in the SMILES sequence simultaneously, producing a holistic representation in which toxicophoric groups are described within the context of the complete molecular architecture.

The qualitative analysis of individual predictions illuminated this distinction vividly. Examining cases where the embedding-based model succeeded while the fingerprint-based model failed, the researchers found compounds rich in phosphate esters and carbamate groups, or polyhalogenated aromatic systems, structural motifs associated with acetylcholinesterase inhibition and modulation of neuronal ion channels, the classic mechanisms of insecticidal action. Conversely, when the fingerprint model won, the compounds tended to belong to well-defined agrochemical classes with established low bee toxicity, such as triazine herbicides and triazole fungicides, where simple class-defining substructures are the dominant signal and local encoding suffices. The two models, in other words, see different things in the same molecules.

That complementarity was quantified as well. Although the two best models achieved similar average performance, the Pearson correlation between their predicted probabilities was only 0.7483, indicating partially overlapping but non-identical predictive patterns. In some cross-validation folds the correlation dropped as low as 0.6417 without any degradation in predictive accuracy, evidence that each representation captures distinct aspects of chemical space. The authors argue that this makes the embedding-based model a strong candidate for ensemble frameworks, in which majority voting, probability averaging, or stacking with a meta-learner could combine the strengths of both representations, potentially alongside graph neural network architectures, to build unified predictive platforms accessible through online tools and mobile applications.

The practical implications extend well beyond the leaderboard. By eliminating the need to integrate multiple descriptor calculation libraries and perform complex variable selection, the transfer learning approach substantially simplifies the modeling pipeline, lowering the barrier for ecotoxicological screening under limited labeled data, precisely the conditions that prevail in pollinator protection. The authors caution that their models were developed and evaluated exclusively within the chemical space of the ApisTox dataset, and they recommend formal applicability domain analysis, using tools such as leverage statistics or nearest-neighbor distances in embedding space, before any regulatory deployment. Future work, they note, should explore full-scale pretrained models and incorporate chronic toxicity endpoints. As honey bee populations continue to decline worldwide under the combined pressures of pesticide exposure, habitat loss, and climate change, tools that can rapidly and reliably flag dangerous chemicals before they reach the field may prove not just convenient but essential.

Subject of Research: Transfer learning with chemical language models for predicting honey bee toxicity

Article Title: Transfer learning for honey bee toxicity prediction: MolFormer versus classical QSAR representations

Article References: de Souza Pinho, A. V., de Alencar Filho, E. B., & de Oliveira Neto, R. F. (2026). Transfer learning for honey bee toxicity prediction: MolFormer versus classical QSAR representations. Ecotoxicology, 35(7), Article 166. https://doi.org/10.1007/s10646-026-03149-x

Image Credits: AI Generated

DOI: 10.1007/s10646-026-03149-x

Keywords: transfer learning, MolFormer, QSAR, honey bee toxicity, Apis mellifera, ApisTox, chemical language models, Morgan fingerprints, PaDEL descriptors, ecotoxicology, pesticide risk assessment, machine learning