GutenOCR is a family of vision-language models designed to serve as a ‘grounded OCR front-end’, providing high-quality text transcription and explicit geometric grounding.
We systematically ablate core mechanisms of Transformers and RNNs, finding that attention-augmented Recurrent Highway Networks outperform standard Transformers on forecasting high-dimensional chaotic systems.
Property-optimizing ACSESS combines diversity-biased sampling with iterative fitness thresholding to discover diverse sets of molecules with favorable properties. Tested on GDB-9 (dipole moment optimization) and NKp fitness landscapes, it outperforms standard genetic algorithms in diversity while matching or exceeding their fitness, using only ~30,000 evaluations to navigate a 300,000-molecule space.
AllChem generates ~5 million synthons by recursively applying ~100 reactions to ~7,000 building blocks, combinatorially representing up to 10^20 complete structures with an A-B-C topology. Topomer shape similarity enables efficient searching of this space, and every hit comes with a proposed synthetic route.
CHX8 exhaustively enumerates all mathematically feasible hydrocarbons with up to eight carbon atoms (77,524 structures), then DFT-optimizes them to identify 31,497 stable molecules. A universal relative strain energy (RSE) metric referenced to cyclohexane serves as a synthesizability proxy. CHX8 covers 16x more C8 hydrocarbons than GDB-13 and reveals that over 90% of novel structures should be synthetically accessible.
FDB-17 contains 10 million fragment-like molecules selected from GDB-17’s 166.4 billion entries. Fragment-likeness filters reduce GDB-17 by 36x to 4.6 billion molecules, then even sampling across (HAC, heteroatoms, stereocenters) triplets produces a 460x further reduction to a manageable, diverse library enriched in 3D-shaped molecules.
GDBMedChem applies medicinal chemistry-inspired functional group and structural complexity filters to GDB-17, reducing 166.4 billion molecules to 17.8 billion, then evenly samples across molecular size, stereochemistry, and polarity to produce 10 million drug-like molecules. 97% of its substructures are absent from known molecule databases.
Buehler and Reymond introduce two molecular complexity measures, MC1 (fraction of non-divalent nodes) and MC2 (count of non-divalent nodes excluding carboxyl groups), derived from analyzing synthesizability patterns in GDB-enumerated molecules. They compare these measures against existing complexity scores across GDB-13s, ZINC, ChEMBL, and COCONUT.
QM9 provides B3LYP/6-31G(2df,p)-level geometric, energetic, electronic, and thermodynamic properties for 133,885 small organic molecules (up to 9 heavy atoms of C, N, O, F) drawn from the GDB-17 chemical universe. It is one of the most widely used benchmarks in molecular machine learning.
Surge is a constitutional isomer generator based on the canonical generation path method, using nauty for graph automorphism computation. Its three-stage pipeline (simple graph generation, vertex coloring for atom assignment, edge multiplicity for bond orders) generates 7-22 million molecules per second, outperforming MOLGEN 5.0 by 42-161x on natural product molecular formulas.
VEHICLe (Virtual Exploratory Heterocyclic Library) is a complete enumeration of 24,867 mono- and bicyclic heteroaromatic ring systems built from C, N, O, S, and H. Of these, only 1,701 have ever appeared in published compounds. A random forest classifier trained on known vs. unknown ring systems predicts that over 3,000 additional ring systems are synthetically tractable.
VQM24 exhaustively enumerates all neutral closed-shell molecules with up to 5 heavy atoms from C, N, O, F, Si, P, S, Cl, Br, yielding 258k constitutional isomers and 578k conformers (836k total). Properties are computed at the wB97X-D3/cc-pVDZ level, with diffusion QMC energies for 10,793 molecules up to 4 heavy atoms. ML models show up to 8x higher errors than on QM9, making VQM24 a more challenging benchmark.