[HTML][HTML] Artificial intelligence to deep learning: machine intelligence approach for drug discovery
Drug designing and development is an important area of research for pharmaceutical
companies and chemical scientists. However, low efficacy, off-target delivery, time …
companies and chemical scientists. However, low efficacy, off-target delivery, time …
Recent applications of deep learning and machine intelligence on in silico drug discovery: methods, tools and databases
The identification of interactions between drugs/compounds and their targets is crucial for
the development of new drugs. In vitro screening experiments (ie bioassays) are frequently …
the development of new drugs. In vitro screening experiments (ie bioassays) are frequently …
From machine learning to deep learning: Advances in scoring functions for protein–ligand docking
Molecule docking has been regarded as a routine tool for drug discovery, but its accuracy
highly depends on the reliability of scoring functions (SFs). With the rapid development of …
highly depends on the reliability of scoring functions (SFs). With the rapid development of …
[HTML][HTML] MONN: a multi-objective neural network for predicting compound-protein interactions and affinities
Computational approaches for understanding compound-protein interactions (CPIs) can
greatly facilitate drug development. Recently, a number of deep-learning-based methods …
greatly facilitate drug development. Recently, a number of deep-learning-based methods …
[HTML][HTML] Cancer drug response profile scan (CDRscan): a deep learning model that predicts drug effectiveness from cancer genomic signature
Y Chang, H Park, HJ Yang, S Lee, KY Lee, TS Kim… - Scientific reports, 2018 - nature.com
In the era of precision medicine, cancer therapy can be tailored to an individual patient
based on the genomic profile of a tumour. Despite the ever-increasing abundance of cancer …
based on the genomic profile of a tumour. Despite the ever-increasing abundance of cancer …
Machine learning for biologics: opportunities for protein engineering, developability, and formulation
Successful biologics must satisfy multiple properties including activity and particular
physicochemical features that are globally defined as developability. These multiple …
physicochemical features that are globally defined as developability. These multiple …
[HTML][HTML] Hidden bias in the DUD-E dataset leads to misleading performance of deep learning in structure-based virtual screening
Recently much effort has been invested in using convolutional neural network (CNN)
models trained on 3D structural images of protein-ligand complexes to distinguish binding …
models trained on 3D structural images of protein-ligand complexes to distinguish binding …
Machine‐learning scoring functions for structure‐based virtual screening
Molecular docking predicts whether and how small molecules bind to a macromolecular
target using a suitable 3D structure. Scoring functions for structure‐based virtual screening …
target using a suitable 3D structure. Scoring functions for structure‐based virtual screening …
[HTML][HTML] DEEPScreen: high performance drug–target interaction prediction with convolutional neural networks using 2-D structural compound representations
The identification of physical interactions between drug candidate compounds and target
biomolecules is an important process in drug discovery. Since conventional screening …
biomolecules is an important process in drug discovery. Since conventional screening …
Molecule property prediction based on spatial graph embedding
Accurate prediction of molecular properties is important for new compound design, which is
a crucial step in drug discovery. In this paper, molecular graph data is utilized for property …
a crucial step in drug discovery. In this paper, molecular graph data is utilized for property …