Computational Chemistry

Three panels comparing sampling strategies in a multi-modal fitness landscape: exhaustive enumeration, genetic algorithm clustering around few peaks, and ACSESS covering all peaks with fewer evaluations

ACSESS: Diverse Optimal Molecules in the SMU

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.

Computational Chemistry

Diagram showing AllChem's combinatorial synthon assembly pipeline: 7,000 building blocks transformed by 100 reactions into 5 million synthons, which combine in A-B-C topology to represent 10^20 structures

AllChem: Generating and Searching 10^20 Structures

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.

Computational Chemistry

CHX8 enumeration pipeline from 77,524 structures to 31,497 stable molecules, example strained scaffolds with RSE values, and box plots of relative strain energy distribution by heavy atom count

CHX8: Complete Eight-Carbon Hydrocarbon Space

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.

Computational Chemistry

FDB-17 filtering pipeline from GDB-17 (166.4B) through fragment filters (4.6B) to even sampling (10M), with bar charts comparing size distribution and Fsp3 shape complexity against commercial fragments

FDB-17: Fragment Database (10M Molecules)

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.

Computational Chemistry

GDBMedChem pipeline from GDB-17 through medicinal chemistry filters to 10M molecules, with Venn diagram showing 97% unique substructures and property comparison against known drugs

GDBMedChem: Drug-Like Subset of GDB-17 (10M 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.

Computational Chemistry

Six molecules with atoms colored by divalent (blue, simple) vs non-divalent (red, complex) nodes, showing increasing MC1 complexity from hexane to pivaloyl methylamine

Molecular Complexity from the GDB Chemical Space

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.

Computational Chemistry

Simulated QM9 property landscape scatter plot of HOMO-LUMO gap vs dipole moment, colored by heavy atom count, with example molecules rendered alongside

QM9: Quantum Chemistry Properties of 134k Molecules

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.

Computational Chemistry

Three-stage canonical generation pipeline (geng, vcolg, multig) alongside a log-scale speed comparison showing Surge outperforming MOLGEN 5.0 by 42-161x across natural product molecular formulas

Surge: Fastest Open-Source Chemical Graph Generator

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.

Computational Chemistry

Grid of heteroaromatic ring systems rendered with RDKit, showing known ring systems in blue-tinted panels and predicted tractable rings in amber-tinted panels

VEHICLe: Heteroaromatic Rings of the Future

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.

Computational Chemistry

VQM24 overview showing 9 included elements with valencies, combinatorial scaling of molecular geometries with heavy atom count, and ML learning curves comparing VQM24 vs QM9 difficulty

VQM24: 836k Molecules at DFT and Diffusion QMC

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.

Computational Chemistry

Diagram showing the Ewald decomposition of long-range interactions into short-range and Fourier-space components for molecular graph neural networks

Ewald Message Passing for Molecular Graphs

Proposes Ewald message passing, a Fourier-space scheme inspired by Ewald summation that captures long-range interactions in molecular graphs. The method is architecture-agnostic and improves energy MAEs by 10% on OC20 and 16% on OE62 across four baseline GNN models.

Computational Chemistry

Caffeine molecular structure with its InChIKey identifier

InChI: The International Chemical Identifier

InChI (International Chemical Identifier) is an open standard from IUPAC that represents molecular structures as hierarchical, layered strings optimized for database interoperability, unique identification, and web search via its hashed InChIKey.