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Drug Design

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Modern machine learning for tackling inverse problems in chemistry: molecular design to realization.

Chemical communications (Cambridge, England)
The discovery of new molecules and materials helps expand the horizons of novel and innovative real-life applications. In pursuit of finding molecules with desired properties, chemists have traditionally relied on experimentation and recently on comb...

Machine learning approaches and their applications in drug discovery and design.

Chemical biology & drug design
This review is focused on several machine learning approaches used in chemoinformatics. Machine learning approaches provide tools and algorithms to improve drug discovery. Many physicochemical properties of drugs like toxicity, absorption, drug-drug ...

Harnessing Deep Learning for Optimization of Lennard-Jones Parameters for the Polarizable Classical Drude Oscillator Force Field.

Journal of chemical theory and computation
The outcomes of computational chemistry and biology research, including drug design, are significantly influenced by the underlying force field (FF) used in molecular simulations. While improved FF accuracy may be achieved via inclusion of explicit t...

Feature Reduction for Molecular Similarity Searching Based on Autoencoder Deep Learning.

Biomolecules
The concept of molecular similarity has been commonly used in rational drug design, where structurally similar molecules are examined in molecular databases to retrieve functionally similar molecules. The most used conventional similarity methods use...

Affinity prediction using deep learning based on SMILES input for D3R grand challenge 4.

Journal of computer-aided molecular design
Modern molecular docking comprises the prediction of pose and affinity. Prediction of docking poses is required for affinity prediction when three-dimensional coordinates of the ligand have not been provided. However, a large number of feature engine...

Artificial Intelligence Technologies for COVID-19 De Novo Drug Design.

International journal of molecular sciences
The recent covid crisis has provided important lessons for academia and industry regarding digital reorganization. Among the fascinating lessons from these times is the huge potential of data analytics and artificial intelligence. The crisis exponent...

Generative machine learning for de novo drug discovery: A systematic review.

Computers in biology and medicine
Recent research on artificial intelligence indicates that machine learning algorithms can auto-generate novel drug-like molecules. Generative models have revolutionized de novo drug discovery, rendering the explorative process more efficient. Several...

Artificial Intelligent Deep Learning Molecular Generative Modeling of Scaffold-Focused and Cannabinoid CB2 Target-Specific Small-Molecule Sublibraries.

Cells
Design and generation of high-quality target- and scaffold-specific small molecules is an important strategy for the discovery of unique and potent bioactive drug molecules. To achieve this goal, authors have developed the deep-learning molecule gene...

Unsupervised Learning in Drug Design from Self-Organization to Deep Chemistry.

International journal of molecular sciences
The availability of computers has brought novel prospects in drug design. Neural networks (NN) were an early tool that cheminformatics tested for converting data into drugs. However, the initial interest faded for almost two decades. The recent succe...

Machine Learning Approaches for Metalloproteins.

Molecules (Basel, Switzerland)
Metalloproteins are a family of proteins characterized by metal ion binding, whereby the presence of these ions confers key catalytic and ligand-binding properties. Due to their ubiquity among biological systems, researchers have made immense efforts...