A graph-grammar cheminformatics workflow expands the carbon fixation reaction network, then uses integer linear programming flow queries to surface short autocatalytic pathways producing Acetyl-CoA and Malate with efficiencies approaching the CETCH cycle.
Building on the gas-phase MB-nrg PEF for polyalanine, Ruihan Zhou and Francesco Paesani add machine-learned 2-body terms for each backbone functional group interacting with water, fit to BSSE-corrected DLPNO-CCSD(T)/aug-cc-pVTZ data, then validate the resulting potential against alanine dipeptide-water dimer scans, free-energy surfaces in explicit MB-pol water, and hydration radial distribution functions.
Ruihan Zhou and co-authors extend the MB-nrg many-body formalism to covalently bonded biomolecules by fragmenting polyalanine into functional-group n-mers, fitting permutationally invariant polynomials to DLPNO-CCSD(T)/aug-cc-pVTZ reference energies, and reproducing alanine dipeptide Ramachandran surfaces, harmonic frequencies, and AceAla9Nme secondary-structure dynamics more faithfully than Amber ff14SB and ff19SB.
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.
A conformation autoencoder converts molecular 3D arrangements into fixed-size latent representations using internal coordinates and graph neural networks, enabling conformer generation and spatial property optimization.
DGCNN introduces the EdgeConv operator, which constructs k-nearest neighbor graphs dynamically in feature space at each network layer. This enables the model to capture both local geometry and long-range semantic relationships for point cloud classification and segmentation.
LSTNet is a deep learning framework for multivariate time series forecasting that uses convolutional layers for local dependencies, a recurrent-skip component for periodic long-term patterns, and an autoregressive component for scale robustness.
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.
An updated description of nauty and introduction of Traces, two programs for graph isomorphism testing and canonical labeling using the individualization-refinement paradigm.
SpeechT5 proposes a unified encoder-decoder pre-training framework that jointly learns from unlabeled speech and text data, achieving strong results on ASR, TTS, speech translation, voice conversion, speech enhancement, and speaker identification.
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.