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.
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.
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.
ChatDrug is a parameter-free framework that combines ChatGPT with retrieval-augmented domain feedback and iterative conversation to edit drugs across small molecules, peptides, and proteins.
ChemCrow augments GPT-4 with 18 chemistry tools to autonomously plan and execute syntheses, discover novel chromophores, and solve diverse chemical reasoning tasks.
DrugAssist fine-tunes Llama2-7B-Chat on over one million molecule pairs for interactive, dialogue-based molecule optimization across six molecular properties.
DrugChat is a prototype system that bridges molecular graph neural networks with large language models for interactive, multi-turn question answering about drug compounds. It trains only a lightweight linear adaptor between a frozen GNN encoder and Vicuna-13B using 143K curated QA pairs from ChEMBL and PubChem.
DrugEx v2 introduces Pareto-based multi-objective optimization and evolutionary exploration strategies into an RNN reinforcement learning framework for de novo drug design toward multiple protein targets.
LatentGAN decouples molecular generation from SMILES syntax by training a Wasserstein GAN on latent vectors from a pretrained heteroencoder, enabling de novo design of drug-like and target-biased compounds.