In a study published in Molecular Diversity, researchers led by Huang Zeng and Bo Qiu of Jiaying University describe a systematic computational framework that pairs five types of molecular fingerprints with both classical machine learning and deep learning algorithms. Their goal was twofold: to rigorously compare how different combinations of chemical representations and algorithms perform at predicting TOP1 inhibition, and to convert the best performers into practical tools that any medicinal chemist can use. The work culminated not just in models but in validated hit compounds, two of which showed meaningful inhibition of the enzyme in laboratory tests.

The foundation of the study is data. The team mined the ChEMBL database for compounds with experimentally measured TOP1 inhibitory activity, assembling a dataset suitable for training classification models. Each molecule was then encoded using five distinct fingerprint families: AtomPairs, which describe pairs of atoms and their topological separation; MACCS keys, a set of 166 structurally predefined substructure features; Morgan fingerprints, circular fingerprints that capture local atomic environments much like extended-connectivity fingerprints; PharmacoPFP, which encodes pharmacophore features rather than pure connectivity; and RDKit descriptors, a collection of calculated physicochemical and topological properties. Because each fingerprint represents a different view of what a molecule is, the choice of representation can matter as much as the choice of algorithm.

On top of these representations, the researchers built a total of forty models spanning classical methods such as support vector machines, random forests, and k-nearest neighbors, alongside deep neural networks. The head-to-head comparison produced a clear result: combinations built on Morgan fingerprints dominated the leaderboard. Under random data splitting, the four top performers were SVM paired with Morgan fingerprints, random forest with Morgan, a deep neural network using MACCS keys, and k-nearest neighbors with Morgan. All four achieved area under the receiver operating characteristic curve values, or ROC-AUC, between 0.93 and 0.94, a level of discrimination that places them firmly in the range useful for prioritizing compounds in virtual screening campaigns.

High scores alone can deceive, particularly in cheminformatics where models sometimes memorize dataset quirks rather than learning genuine structure-activity relationships. To guard against this, the team applied Y-scrambling, a validation technique in which the activity labels are randomly shuffled and models are retrained. If the original models had been learning noise, their scrambled counterparts would perform equally well. They did not, supporting the conclusion that the top models had captured real, non-random relationships between molecular structure and TOP1 inhibition. The authors are also candid about a second, more sobering test: when compounds were split by molecular scaffold rather than randomly, AUC values dropped to between 0.67 and 0.82. This scaffold-split performance reveals that the models are less reliable when asked to extrapolate to chemically novel frameworks, a well-known limitation of fingerprint-based approaches that the researchers explicitly quantify rather than hide.

Interpretability was another pillar of the work. Black-box predictions carry little weight in drug design unless chemists can see what drives them. Using SHAP analysis, a game-theoretic method that assigns each molecular feature a contribution to individual predictions, the team identified the key structural elements that the models associated with TOP1 inhibition. This step transforms the models from opaque ranking machines into sources of chemical hypotheses, allowing medicinal chemists to see which substructures and descriptor values push a compound toward or away from the predicted active class, and potentially to use those insights when designing next-generation analogs.

What distinguishes this study from many computational papers is its insistence on usability. The researchers deployed their best models as a freely accessible web application, reachable at drugpred.top:5000, where users can input a molecule as a SMILES string, the standard text notation for chemical structures, or simply draw the structure in a browser and receive a prediction of TOP1 inhibitory activity. For laboratories that cannot or prefer not to rely on an internet connection, standalone desktop executables are available for download on GitHub at github.com/zenghuang8006/TOP1-inhibitor-prediction. The authors note that earlier AI-driven studies of TOP1 inhibitors lacked both systematic fingerprint and algorithm comparisons and accessible predictive tools, and this pipeline is designed to fill both gaps at once.

The real proof, however, came from the laboratory. The team screened a commercial library of 189,554 compounds from SPECS using their computational framework, then took the highest-ranking candidates into in vitro testing. Two compounds, designated AG60 and AI61, emerged as potential TOP1 inhibitor hits. At a concentration of 400 micromolar, AG60 inhibited the enzyme by 64 percent, while AI61 achieved 90 percent inhibition. Converting a computational prediction into experimentally confirmed enzyme inhibition, even at moderate potency, is a meaningful step; the compounds now represent starting points, or hits, that can be chemically optimized into more potent and selective leads through medicinal chemistry.

The biological context underscores why this target continues to attract attention. TOP1 is essential in proliferating cells, and its inhibition selectively harms cancer cells that cannot tolerate the resulting DNA damage. Approved TOP1 poisons remain mainstays of treatment for colorectal, ovarian, and other cancers, and newer agents such as antibody-drug conjugates carrying TOP1 inhibitor payloads have expanded the target’s therapeutic reach. But resistance, toxicity, and the pharmacological limitations of existing scaffolds keep the demand for chemical novelty high. The authors’ own prior work, including the computational identification of FTY720 and COH29 as TOP1 catalytic inhibitors, illustrates a sustained research program aimed at expanding the toolbox of TOP1-directed chemistry.

For the broader field, the study offers a template as much as a set of hits. It demonstrates that fingerprint choice can be systematically benchmarked, that interpretability tools like SHAP can be integrated into the screening workflow, and that honest reporting of scaffold-split performance should accompany headline accuracy figures. It also shows that the distance between a machine learning model and a wet-lab result can be shortened when prediction tools are built for practicing chemists rather than for other data scientists. The forty models, the validated hits AG60 and AI61, and the freely available web and desktop predictors together form a practical framework that other target-focused screening campaigns can adapt. As artificial intelligence continues to compress the early stages of drug discovery, studies that pair rigorous benchmarking with experimental follow-through, and that are transparent about both strengths and limitations, offer the clearest picture of what these methods can actually deliver for anticancer drug development.

Subject of Research: AI-driven screening of topoisomerase I inhibitors using machine learning, deep learning and molecular fingerprints

Article Title: Integrating machine learning and deep learning with multiple molecular fingerprints for topoisomerase I inhibitor screening and lead identification

Article References: Integrating machine learning and deep learning with multiple molecular fingerprints for topoisomerase I inhibitor screening and lead identification. (n.d.). https://doi.org/10.1007/s11030-026-11709-w

Image Credits: AI Generated

DOI: 10.1007/s11030-026-11709-w

Keywords: topoisomerase I, TOP1 inhibitors, machine learning, deep learning, molecular fingerprints, virtual screening, drug discovery, SHAP, ChEMBL, anticancer targets, SPECS compounds, QSAR