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.
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.
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.
A systematic study of data transfer techniques (joint training, self-training, pre-training plus fine-tuning) applied to Transformer-based retrosynthesis. Pre-training on USPTO-Full followed by fine-tuning on USPTO-50K achieves the best results, improving top-1 accuracy from 35.3% to 57.4%.
This 2016 paper first proposed treating organic reaction prediction as a neural machine translation problem, using a GRU-based sequence-to-sequence model with attention to translate reactant SMILES strings into product SMILES strings.
ReactionT5 introduces a two-stage pretraining pipeline (compound then reaction) on the Open Reaction Database, enabling competitive product and yield prediction with as few as 30 fine-tuning reactions.
A systematic study of SMILES augmentation strategies for molecular property prediction, showing that augmentation consistently improves CNN and RNN performance and that prediction variance across SMILES correlates with model uncertainty.
MTL-BERT pretrains a BERT model on 1.7M unlabeled SMILES, then fine-tunes jointly on 60 ADMET and molecular property tasks using SMILES enumeration as data augmentation in all phases.
LLM-Prop uses the encoder half of T5, fine-tuned on Robocrystallographer text descriptions, to predict crystal properties. It outperforms GNN baselines like ALIGNN on band gap and volume prediction while using fewer parameters.