The paper investigates how different nearest‑neighbour retrieval methods perform as baselines for predicting molecular fingerprints from MS/MS spectra. It systematically compares several variants that differ in the information assumed available at inference, showing that these assumptions significantly affect performance. The goal is to establish stricter baselines for more rigorous benchmarking and progress measurement in the field.
arXiv:2604. 27810v2 Announce Type: replace Abstract: Computational molecular representations underpin virtual screening, property prediction, and materials discovery.
TopU-LBVS is a new multi‑target benchmark for ligand‑based virtual screening that addresses shortcomings of existing datasets by using hard‑negative decoys and a fixed 1:40 active‑to‑decoy ratio. It covers 93 protein targets across seven classes, provides three evaluation protocols (full, low‑data, and mini), and includes curated ChEMBL‑35 bioactivity data with property‑matched, structurally similar decoys. The benchmark demonstrates that performance drops sharply when moving from random‑decoy to hard‑negative evaluation, and it releases data, splits, code, and baseline implementations for reproducible comparison.
The paper evaluates molecular representations used in cheminformatics and small‑molecule machine learning, such as fingerprints and learned embeddings. It finds that current state‑of‑the‑art embedding models do not outperform traditional fingerprints or physicochemical descriptors in capturing graph structure, 3D shape, or ADMET similarity. Additionally, fingerprints exhibit significant limitations, with 225 million to 4 billion molecules in the 10.8 billion‑molecule Enamine REAL database sharing non‑unique representations, which impacts virtual screening.
TopU-LBVS is a new multi‑target benchmark for ligand‑based virtual screening that addresses shortcomings of previous datasets by using hard‑negative decoys and a fixed 1:40 active‑to‑decoy ratio. It covers 93 protein targets across seven classes, provides three evaluation protocols (full, low‑data, and mini), and includes curated ChEMBL‑35 bioactivity data with property‑matched, structurally similar decoys to reduce shortcut learning. The benchmark comes with released data, fixed splits, evaluation code, and baseline implementations for reproducible comparison of LBVS and molecular representation methods.
arXiv:2603. 10950v2 Announce Type: replace Abstract: Machine learning methods for identifying molecular structures from tandem mass spectra (MS/MS) have advanced rapidly, yet current approaches still exhibit significant error rates.
arXiv:2607. 23607v1 Announce Type: new Abstract: Molecular structure elucidation from tandem mass spectra (MS/MS) is a central inverse problem in analytical chemistry.
arXiv:2608. 02027v1 Announce Type: new Abstract: We present scikit-fingerprints, a comprehensive, fully scikit-learn compatible library for molecular machine learning in Python, based on RDKit.
arXiv:2609.15611v1 Announce Type: cross Abstract: Molecular property prediction requires representations that generalize from limited labeled data to structurally novel compounds. Existing molecular...
arXiv:2510. 14217v2 Announce Type: replace Abstract: The spectral properties of feature embeddings offer critical insights into model generalization and representation quality.
GEMS (Guided Editing of Molecular Structures) is a new method for refining candidate small‑molecule structures from molecular formulas and tandem mass spectra. It uses learned 2‑switch bond edits that preserve the formula and valence, guided by a neural policy trained on shortest edit paths and fingerprint predictions from the MIST model. In benchmark tests, GEMS outperforms existing baselines, achieving higher Hit@1/Hit@10 rates and dramatically faster identification times compared to prior approaches.
arXiv:2602. 22822v3 Announce Type: replace Abstract: Tandem mass spectrometry (MS/MS) is central to small molecule identification, but current deep learning systems for spectrum prediction still remain difficult to evaluate and deploy in practice.
The paper introduces SBMEX, a new skeleton‑based minutiae detection method that replaces legacy C/C++ code with a fast, deterministic algorithm integrated into the open‑source pyfing package. SBMEX uses a dual Look‑Up Table architecture to eliminate runtime neighborhood scanning during Crossing Number computation and skeleton tracking, and adds a continuous quality scoring framework based on tracking path length, dual ridge‑valley skeleton fusion, and spatial density decay. Evaluation on NIST SD302 shows that SBMEX matches or surpasses traditional open‑source baselines in accuracy while dramatically reducing detection latency compared to classical Python implementations.