3 papers
cs.LG2025
Universal Laboratory Model: prognosis of abnormal clinical outcomes based on routine tests
Pavel Karpov, Ilya Petrenkov, Ruslan Raiman
Clinical laboratory results are ubiquitous in any diagnosis making. Predicting abnormal values of not prescribed tests based on the results of performed tests looks intriguing, as…
cs.LG2020
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)…
q-bio.QM2019
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…