compute molecular fingerprints

This work addresses the limitations of conventional molecular fingerprints, which are predominantly based on two-dimensional structures and thus fail to distinguish stereoisomers and conformers, while existing three-dimensional approaches often suffer from high computational cost or heavy data dependence. The authors propose a physics-inspired 3D molecular fingerprint that represents a molecule as a fully connected 3D graph, where edge weights encode heuristic physical interactions. By performing eigendecomposition on the graph Laplacian matrix, the method yields a fixed-length descriptor invariant to both atomic permutations and E(3) transformations. This approach uniquely integrates spectral graph theory with physically motivated 3D graph representations, achieving state-of-the-art performance across multiple chemical datasets. It offers strong interpretability, geometric awareness, and low computational complexity, making it well-suited for efficient large-scale chemical space screening and applicability domain analysis.

Molecular images in chemical patents and related literature are difficult to retrieve via text-based search due to the absence of machine-readable structural representations. Method: This paper proposes a novel vision-based fingerprinting paradigm that bypasses molecular graph reconstruction. It employs learned instance segmentation to precisely localize functional groups and carbon skeletons, followed by substructure encoding to generate disentangled visual fingerprint embeddings—eliminating reliance on conventional OCR and structure recognition pipelines. Contribution/Results: The method exhibits superior robustness to stylistic variations—including distorted, hand-drawn, and low-resolution chemical diagrams. Evaluated on a multi-source chemical image dataset, it achieves state-of-the-art performance in visual retrieval, outperforming existing approaches (e.g., OCSR and conventional fingerprint methods), particularly on complex images. Retrieval accuracy improvements are statistically significant across challenging scenarios. This work establishes an efficient, scalable framework for chemical image understanding, with direct implications for drug discovery and materials science.

To address the low information density of 2D graph representations and their inability to model stereoelectronic effects in molecular machine learning, this work introduces a novel molecular graph representation that explicitly incorporates quantum-chemical stereoelectronic features—such as orbital interactions. Leveraging a dual-graph neural network architecture coupled with physics-informed representation learning, our method is the first to encode stereoelectronic information directly into the message-passing process without relying on costly quantum mechanical calculations. The approach significantly improves prediction accuracy across diverse molecular properties and demonstrates cross-scale generalizability—successfully transferring from small-molecule training sets to ultra-large systems such as proteins. We release an open-source web platform (simg.cheme.cmu.edu) enabling real-time stereoelectronic analysis and interactive visualization. The framework achieves state-of-the-art predictive performance while maintaining strong interpretability grounded in physical principles.

The Python ecosystem lacks an efficient, user-friendly, and feature-complete open-source library for molecular fingerprint computation, hindering reproducibility and scalability in cheminformatics tasks such as property prediction and virtual screening. To address this, we introduce FingerPy—the first industrial-grade, open-source molecular fingerprinting library fully compliant with the scikit-learn API and supporting over 30 mainstream fingerprint types. Its core innovations include deep integration of underlying cheminformatics engines (e.g., RDKit), a hybrid multiprocessing/multithreading parallelization strategy, and algorithmic and memory-access optimizations specifically designed for batch fingerprint generation. Experimental evaluation demonstrates that FingerPy achieves state-of-the-art computational performance on widely used fingerprints (e.g., ECFP4, MACCS), outperforming existing open-source tools by 2–5×. Moreover, its native scikit-learn compatibility enables seamless integration into machine learning pipelines, significantly enhancing modeling efficiency and cross-platform reproducibility.

The Molecular Assembly (MA) index—a key metric quantifying structural complexity and bioactivity potential—is computationally intractable for molecules >500 Da using existing algorithms, suffering from poor convergence and lacking a unified measure of global scaffold reuse. Method: We propose the first efficient and exact algorithmic framework for computing MA—defined as the minimum number of constrained assembly steps—by introducing an assembly-state edge-list array data structure, integrating subgraph enumeration, dynamic programming, and branch-and-bound optimization to enable subgraph reuse and aggressive pruning of invalid states. Contribution/Results: Our method enables exact assembly-path reconstruction for large-scale natural product libraries (e.g., hundreds of thousands of molecules in COCONUT), achieving substantial speedups and reduced memory footprint over baseline approaches. It provides the first scalable, rigorous tool for quantifying molecular structural complexity, directly supporting biomarker identification and drug discovery.

This study addresses the lack of systematic comparison among molecular encoding methods in terms of both predictive performance and interpretability for drug property prediction. The authors propose a hybrid model combining multilayer perceptrons and Transformer encoders (MLP+TL) to comprehensively evaluate topological fingerprints, substructure-based fingerprints (e.g., MACCS, PubChem), and string representations across seven molecular datasets and multiple biologically relevant classification tasks. Innovatively leveraging the model’s intrinsic attention weights—without relying on external interpretation tools—the work identifies critical chemical moieties, revealing mechanistic insights such as the influence of hydroxyl groups on blood–brain barrier permeability and Salmonella mutagenicity. The model achieves average AUC scores exceeding 0.9 in toxicity, mutagenicity, and side-effect prediction tasks, demonstrating both high predictive accuracy and inherent chemical interpretability.

This work addresses the inconsistency in predictions and explanations arising from multiple graph representations of the same molecule in molecular graph machine learning, which violates chemical identity. To resolve this, the authors propose InChIfied Invariants—strictly invariant features constructed at node, edge, and graph levels based on the International Chemical Identifier (InChI)—that inherently guarantee identical representations, predictions, and attributions for chemically equivalent graphs. Evaluation on the large-scale PubChem Substances dataset demonstrates that the method achieves consistent representations for 99.62% of chemically equivalent graph pairs, a dramatic improvement over the 0.35% consistency achieved by conventional Daylight invariants. Furthermore, it maintains competitive predictive performance on MoleculeNet benchmark tasks while significantly enhancing model interpretability and chemical plausibility.

This work proposes Hyperdimensional Fingerprints (HDF), a novel molecular representation that overcomes the structural information loss inherent in traditional hashed fingerprints while avoiding the high computational cost and data dependency of graph neural networks. By introducing hyperdimensional computing to molecular encoding, HDF leverages algebraic operations in high-dimensional vector spaces to generate deterministic, training-free molecular embeddings, replacing conventional message-passing mechanisms. The resulting representations achieve high structural fidelity, surpassing classical fingerprints in most molecular property prediction tasks. Notably, even a 32-dimensional HDF exhibits a correlation of 0.9 with graph edit distance and significantly enhances sample efficiency in Bayesian optimization.

This work addresses the incompatibility between existing open-source cheminformatics tools and the scikit-learn ecosystem, which hinders unified and reusable molecular machine learning workflows. To bridge this gap, the authors introduce a Python library built on RDKit that fully adheres to the scikit-learn API specification. For the first time, core cheminformatics functionalities—including molecular fingerprinting, filters, similarity metrics, applicability domain estimation, and data splitting—are encapsulated within a consistent, composable interface. This design enables efficient computation and custom extensibility, supporting an end-to-end pipeline from SMILES inputs to deployable models. The proposed framework substantially enhances development efficiency, reproducibility, and system integration in molecular modeling, effectively reconciling cheminformatics with mainstream machine learning ecosystems.

This work addresses the lack of systematic evaluation for symbolic, verifiable reasoning over molecular graph structures in current chemical large language models. Existing benchmarks often suffer from label bias or information leakage, hindering precise diagnosis of model shortcomings. To bridge this gap, we propose MolecularIQ—the first evaluation framework specifically designed for symbolic reasoning on molecular graphs. By integrating molecular graph representations, symbolic logic verification, and carefully structured reasoning tasks, MolecularIQ establishes a fine-grained benchmark that effectively uncovers systematic failure modes of contemporary models across specific molecular structures and reasoning challenges. This framework provides interpretable diagnostic insights and actionable directions for developing chemical large language models with faithful structural understanding capabilities.