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Small Molecule Libraries

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Computational prediction of plasma protein binding of cyclic peptides from small molecule experimental data using sparse modeling techniques.

BMC bioinformatics
BACKGROUND: Cyclic peptide-based drug discovery is attracting increasing interest owing to its potential to avoid target protein depletion. In drug discovery, it is important to maintain the biostability of a drug within the proper range. Plasma prot...

A computational strategy for finding novel targets and therapeutic compounds for opioid dependence.

PloS one
Opioids are widely used for treating different types of pains, but overuse and abuse of prescription opioids have led to opioid epidemic in the United States. Besides analgesic effects, chronic use of opioid can also cause tolerance, dependence, and ...

Predictive classifier models built from natural products with antimalarial bioactivity using machine learning approach.

PloS one
In view of the vast number of natural products with potential antiplasmodial bioactivity and cost of conducting antiplasmodial bioactivity assays, it may be judicious to learn from previous antiplasmodial bioassays and predict bioactivity of these na...

RASPELD to Perform High-End Screening in an Academic Environment toward the Development of Cancer Therapeutics.

ChemMedChem
The identification of compounds for dissecting biological functions and the development of novel drug molecules are central tasks that often require screening campaigns. However, the required architecture is cost- and time-intensive. Herein we descri...

Toxic Colors: The Use of Deep Learning for Predicting Toxicity of Compounds Merely from Their Graphic Images.

Journal of chemical information and modeling
The majority of computational methods for predicting toxicity of chemicals are typically based on "nonmechanistic" cheminformatics solutions, relying on an arsenal of QSAR descriptors, often vaguely associated with chemical structures, and typically ...

Deep reinforcement learning for de novo drug design.

Science advances
We have devised and implemented a novel computational strategy for de novo design of molecules with desired properties termed ReLeaSE (Reinforcement Learning for Structural Evolution). On the basis of deep and reinforcement learning (RL) approaches, ...

Development of Ligand-based Big Data Deep Neural Network Models for Virtual Screening of Large Compound Libraries.

Molecular informatics
High-performance ligand-based virtual screening (VS) models have been developed using various computational methods, including the deep neural network (DNN) method. There are high expectations for exploration of the advanced capabilities of DNN to im...

Redefining the Protein Kinase Conformational Space with Machine Learning.

Cell chemical biology
Protein kinases are dynamic, adopting different conformational states that are critical for their catalytic activity. We assess a range of structural features derived from the conserved αC helix and DFG motif to define the conformational space of the...

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...