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Found 6 papers, 5 papers with code
Atom-Level Optical Chemical Structure Recognition with Limited Supervision
Identifying the chemical structure from a graphical representation, or image, of a molecule is a challenging pattern recognition task that would greatly benefit drug development.
Weakly Supervised Knowledge Transfer with Probabilistic Logical Reasoning for Object Detection
In this work, we propose ProbKT, a framework based on probabilistic logical reasoning that allows to train object detection models with arbitrary types of weak supervision.
Industry-Scale Orchestrated Federated Learning for Drug Discovery
To apply federated learning to drug discovery we developed a novel platform in the context of European Innovative Medicines Initiative (IMI) project MELLODDY (grant n{\deg}831472), which was comprised of 10 pharmaceutical companies, academic research labs, large industrial companies and startups.
SparseChem: Fast and accurate machine learning model for small molecules
SparseChem provides fast and accurate machine learning models for biochemical applications.
Self-Labeling of Fully Mediating Representations by Graph Alignment
In this paper we investigate the scenario of domain adaptation from the source domain where we have access to the expensive labels $V$ to the target domain where only normal labels W are available.
ChemGrapher: Optical Graph Recognition of Chemical Compounds by Deep Learning
Many thousands of scientific articles in chemistry and pharmaceutical sciences have investigated chemical compounds, but in cases the details of the structure of these chemical compounds is published only as an images.
