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Pharmaceutical Machine Learning: Virtual High-Throughput Screens Identifying Promising and Economical Small Molecule Inhibitors of Complement Factor C1s.

Biomolecules
When excessively activated, C1 is insufficiently regulated, which results in tissue damage. Such tissue damage causes the complement system to become further activated to remove the resulting tissue damage, and a vicious cycle of activation/tissue da...

Knowledge-Based Conformer Generation Using the Cambridge Structural Database.

Journal of chemical information and modeling
Fast generation of plausible molecular conformations is central to molecular modeling. This paper presents an approach to conformer generation that makes extensive use of the information available in the Cambridge Structural Database. By using geomet...

Inner and Outer Recursive Neural Networks for Chemoinformatics Applications.

Journal of chemical information and modeling
Deep learning methods applied to problems in chemoinformatics often require the use of recursive neural networks to handle data with graphical structure and variable size. We present a useful classification of recursive neural network approaches into...

SCScore: Synthetic Complexity Learned from a Reaction Corpus.

Journal of chemical information and modeling
Several definitions of molecular complexity exist to facilitate prioritization of lead compounds, to identify diversity-inducing and complexifying reactions, and to guide retrosynthetic searches. In this work, we focus on synthetic complexity and ref...

Potent pairing: ensemble of long short-term memory networks and support vector machine for chemical-protein relation extraction.

Database : the journal of biological databases and curation
Biomedical researchers regularly discover new interactions between chemical compounds/drugs and genes/proteins, and report them in research literature. Having knowledge about these interactions is crucially important in many research areas such as pr...

Extracting chemical-protein relations using attention-based neural networks.

Database : the journal of biological databases and curation
Relation extraction is an important task in the field of natural language processing. In this paper, we describe our approach for the BioCreative VI Task 5: text mining chemical-protein interactions. We investigate multiple deep neural network (DNN) ...

A Computational-Based Method for Predicting Drug-Target Interactions by Using Stacked Autoencoder Deep Neural Network.

Journal of computational biology : a journal of computational molecular cell biology
Identifying the interaction between drugs and target proteins is an important area of drug research, which provides a broad prospect for low-risk and faster drug development. However, due to the limitations of traditional experiments when revealing d...

Novel Method Proposing Chemical Structures with Desirable Profile of Activities Based on Chemical and Protein Spaces.

Molecular informatics
Active molecules among numerous chemical structures in a chemical database can be searched easily by statistical prediction of compound-protein interactions. However, constructing a simple prediction model against one protein does not aid drug design...

Bias-Free Chemically Diverse Test Sets from Machine Learning.

ACS combinatorial science
Current benchmarking methods in quantum chemistry rely on databases that are built using a chemist's intuition. It is not fully understood how diverse or representative these databases truly are. Multivariate statistical techniques like archetypal an...