Dash et al., 2021 - Google Patents
Using domain-knowledge to assist lead discovery in early-stage drug designDash et al., 2021
View PDF- Document ID
- 10080351278004071476
- Author
- Dash T
- Srinivasan A
- Vig L
- Roy A
- Publication year
- Publication venue
- International Conference on Inductive Logic Programming
External Links
Snippet
We are interested in generating new small molecules which could act as inhibitors of a biological target, when there is limited prior information on target-specific inhibitors. This form of drug-design is assuming increasing importance with the advent of new disease …
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06N—COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N99/00—Subject matter not provided for in other groups of this subclass
- G06N99/005—Learning machines, i.e. computer in which a programme is changed according to experience gained by the machine itself during a complete run
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06F—ELECTRICAL DIGITAL DATA PROCESSING
- G06F17/00—Digital computing or data processing equipment or methods, specially adapted for specific functions
- G06F17/30—Information retrieval; Database structures therefor; File system structures therefor
- G06F17/30861—Retrieval from the Internet, e.g. browsers
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06F—ELECTRICAL DIGITAL DATA PROCESSING
- G06F17/00—Digital computing or data processing equipment or methods, specially adapted for specific functions
- G06F17/30—Information retrieval; Database structures therefor; File system structures therefor
- G06F17/3061—Information retrieval; Database structures therefor; File system structures therefor of unstructured textual data
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06F—ELECTRICAL DIGITAL DATA PROCESSING
- G06F19/00—Digital computing or data processing equipment or methods, specially adapted for specific applications
- G06F19/10—Bioinformatics, i.e. methods or systems for genetic or protein-related data processing in computational molecular biology
- G06F19/28—Bioinformatics, i.e. methods or systems for genetic or protein-related data processing in computational molecular biology for programming tools or database systems, e.g. ontologies, heterogeneous data integration, data warehousing or computing architectures
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06N—COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N5/00—Computer systems utilising knowledge based models
- G06N5/02—Knowledge representation
- G06N5/022—Knowledge engineering, knowledge acquisition
- G06N5/025—Extracting rules from data
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06F—ELECTRICAL DIGITAL DATA PROCESSING
- G06F19/00—Digital computing or data processing equipment or methods, specially adapted for specific applications
- G06F19/70—Chemoinformatics, i.e. data processing methods or systems for the retrieval, analysis, visualisation, or storage of physicochemical or structural data of chemical compounds
- G06F19/708—Chemoinformatics, i.e. data processing methods or systems for the retrieval, analysis, visualisation, or storage of physicochemical or structural data of chemical compounds for data visualisation, e.g. molecular structure representations, graphics generation, display of maps or networks or other visual representations
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06F—ELECTRICAL DIGITAL DATA PROCESSING
- G06F19/00—Digital computing or data processing equipment or methods, specially adapted for specific applications
- G06F19/10—Bioinformatics, i.e. methods or systems for genetic or protein-related data processing in computational molecular biology
- G06F19/16—Bioinformatics, i.e. methods or systems for genetic or protein-related data processing in computational molecular biology for molecular structure, e.g. structure alignment, structural or functional relations, protein folding, domain topologies, drug targeting using structure data, involving two-dimensional or three-dimensional structures
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06F—ELECTRICAL DIGITAL DATA PROCESSING
- G06F19/00—Digital computing or data processing equipment or methods, specially adapted for specific applications
- G06F19/10—Bioinformatics, i.e. methods or systems for genetic or protein-related data processing in computational molecular biology
- G06F19/24—Bioinformatics, i.e. methods or systems for genetic or protein-related data processing in computational molecular biology for machine learning, data mining or biostatistics, e.g. pattern finding, knowledge discovery, rule extraction, correlation, clustering or classification
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06N—COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N3/00—Computer systems based on biological models
- G06N3/02—Computer systems based on biological models using neural network models
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06F—ELECTRICAL DIGITAL DATA PROCESSING
- G06F19/00—Digital computing or data processing equipment or methods, specially adapted for specific applications
- G06F19/10—Bioinformatics, i.e. methods or systems for genetic or protein-related data processing in computational molecular biology
- G06F19/12—Bioinformatics, i.e. methods or systems for genetic or protein-related data processing in computational molecular biology for modelling or simulation in systems biology, e.g. probabilistic or dynamic models, gene-regulatory networks, protein interaction networks or metabolic networks
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06N—COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N5/00—Computer systems utilising knowledge based models
- G06N5/04—Inference methods or devices
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06N—COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N7/00—Computer systems based on specific mathematical models
- G06N7/005—Probabilistic networks
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06F—ELECTRICAL DIGITAL DATA PROCESSING
- G06F15/00—Digital computers in general; Data processing equipment in general
- G06F15/18—Digital computers in general; Data processing equipment in general in which a programme is changed according to experience gained by the computer itself during a complete run; Learning machines
Similar Documents
Publication | Publication Date | Title |
---|---|---|
Deng et al. | Artificial intelligence in drug discovery: applications and techniques | |
Arús-Pous et al. | SMILES-based deep generative scaffold decorator for de-novo drug design | |
Cortés-Ciriano et al. | Deep confidence: a computationally efficient framework for calculating reliable prediction errors for deep neural networks | |
Chen et al. | Chemical toxicity prediction based on semi-supervised learning and graph convolutional neural network | |
Kwon et al. | Uncertainty-aware prediction of chemical reaction yields with graph neural networks | |
US20200342953A1 (en) | Target molecule-ligand binding mode prediction combining deep learning-based informatics with molecular docking | |
Li et al. | Sentence constituent-aware aspect-category sentiment analysis with graph attention networks | |
Morris et al. | Predicting binding from screening assays with transformer network embeddings | |
Stålring et al. | AZOrange-High performance open source machine learning for QSAR modeling in a graphical programming environment | |
Dash et al. | Incorporating symbolic domain knowledge into graph neural networks | |
Pinheiro et al. | Smiclr: Contrastive learning on multiple molecular representations for semisupervised and unsupervised representation learning | |
Kim et al. | Bayesian neural network with pretrained protein embedding enhances prediction accuracy of drug-protein interaction | |
Feinstein et al. | Uncertainty-informed deep transfer learning of perfluoroalkyl and polyfluoroalkyl substance toxicity | |
Ucak et al. | Substructure-based neural machine translation for retrosynthetic prediction | |
Kang et al. | Predictive modeling of NMR chemical shifts without using atomic-level annotations | |
Pan et al. | SubMDTA: drug target affinity prediction based on substructure extraction and multi-scale features | |
Abate et al. | Graph neural networks for conditional de novo drug design | |
Dash et al. | Inclusion of domain-knowledge into gnns using mode-directed inverse entailment | |
Yuan et al. | Protein-ligand binding affinity prediction model based on graph attention network | |
Glauer et al. | Interpretable ontology extension in chemistry | |
Wang et al. | A transformer-based generative model for de novo molecular design | |
Dash et al. | Using domain-knowledge to assist lead discovery in early-stage drug design | |
Nemoto et al. | Investigation of chemical structure recognition by encoder–decoder models in learning progress | |
Wang et al. | Molecular property prediction by contrastive learning with attention-guided positive sample selection | |
Bharti et al. | GCAC: galaxy workflow system for predictive model building for virtual screening |