350 citations · 714 across the 9 of their papers we have counts for
9 papers
SEISMO: Explanation-Aware, Trajectory-Conditioned LLM Agents for Sample-Efficient Molecular Optimisation
Fabian P. Krüger, Andrea Hunklinger, Adrian Wolny +3
Optimizing molecules to achieve desired properties is a central bottleneck across the chemical sciences, particularly in the pharmaceutical industry, where it underlies the discove…
Models Matter: The Impact of Single-Step Retrosynthesis on Synthesis Planning
Paula Torren-Peraire, Alan Kai Hassen, Samuel Genheden +4
Retrosynthesis consists of breaking down a chemical compound recursively step-by-step into molecular precursors until a set of commercially available molecules is found with the go…
Mind the Retrosynthesis Gap: Bridging the divide between Single-step and Multi-step Retrosynthesis Prediction
Alan Kai Hassen, Paula Torren-Peraire, Samuel Genheden +3
Retrosynthesis is the task of breaking down a chemical compound recursively step-by-step into molecular precursors until a set of commercially available molecules is found. Consequ…
Beyond Chemical 1D knowledge using Transformers
Ruud van Deursen, Igor V. Tetko, Guillaume Godin
In the present paper we evaluated efficiency of the recent Transformer-CNN models to predict target properties based on the augmented stereochemical SMILES. We selected a well-know…
State-of-the-Art Augmented NLP Transformer models for direct and single-step retrosynthesis
Igor V. Tetko, Pavel Karpov, Ruud Van Deursen +1
We investigated the effect of different training scenarios on predicting the (retro)synthesis of chemical compounds using a text-like representation of chemical reactions (SMILES)…
Transformer-CNN: Fast and Reliable tool for QSAR
Pavel Karpov, Guillaume Godin, Igor V. Tetko
We present SMILES-embeddings derived from the internal encoder state of a Transformer [1] model trained to canonize SMILES as a Seq2Seq problem. Using a CharNN [2] architecture upo…
Multitask Learning On Graph Neural Networks Applied To Molecular Property Predictions
Fabio Capela, Vincent Nouchi, Ruud Van Deursen +2
Prediction of molecular properties, including physico-chemical properties, is a challenging task in chemistry. Herein we present a new state-of-the-art multitask prediction method…
GEN: Highly Efficient SMILES Explorer Using Autodidactic Generative Examination Networks
Ruud van Deursen, Peter Ertl, Igor V. Tetko +1
Recurrent neural networks have been widely used to generate millions of de novo molecules in a known chemical space. These deep generative models are typically setup with LSTM or G…
Synergy Effect between Convolutional Neural Networks and the Multiplicity of SMILES for Improvement of Molecular Prediction
Talia B. Kimber, Sebastian Engelke, Igor V. Tetko +2
In our study, we demonstrate the synergy effect between convolutional neural networks and the multiplicity of SMILES. The model we propose, the so-called Convolutional Neural Finge…