A locally-built, LLM-digested index of recent arXiv papers in quant finance, geometry/topology, and statistical ML — keyword search served straight from SQLite on this machine.
This paper examines data transfer methods to improve off-the-shelf Transformer models for retrosynthesis.
METRO predicts reactions using minimal templates, reducing computational overhead and achieving state-of-the-art results.
Paper introduces a graph-based approach for retrosynthesis prediction.
Hopfield networks improve reaction template prediction for few/zero-shot scenarios.
Retrosynthesis is one of the fundamental problems in organic chemistry. The task is to identify reactants that can be used to synthesize a specified product molecule. Recently, computer-aided retrosynthesis is finding renewed interest from both chemistry and computer science communities. Most existing approaches rely o…
G2Gs transforms target molecules into reactants without templates, improving accuracy.
Improved chemical reaction prediction using augmented NLP models.
Bayesian algorithm discovers synthetic routes from target molecules.
Computer-assisted synthesis planning aims to help chemists find better reaction pathways faster. Finding viable and short pathways from sugar molecules to value-added chemicals can be modeled as a retrosynthesis planning problem with a catalyst allowed. This is a crucial step in efficient biomass conversion. The tradit…
We present an extension of our Molecular Transformer architecture combined with a hyper-graph exploration strategy for automatic retrosynthesis route planning without human intervention. The single-step retrosynthetic model sets a new state of the art for predicting reactants as well as reagents, solvents and catalysts…
MEGAN models chemical reactions as graph edits, improving synthesis planning.
We propose a new model for making generalizable and diverse retrosynthetic reaction predictions. Given a target compound, the task is to predict the likely chemical reactants to produce the target. This generative task can be framed as a sequence-to-sequence problem by using the SMILES representations of the molecules.…
Deep generative models are able to suggest new organic molecules by generating strings, trees, and graphs representing their structure. While such models allow one to generate molecules with desirable properties, they give no guarantees that the molecules can actually be synthesized in practice. We propose a new molecu…
CogMol designs novel drug-like molecules for SARS-CoV-2 targets.