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Hydrophobic and Hydrophilic Interactions

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Predicting Small Molecule Transfer Free Energies by Combining Molecular Dynamics Simulations and Deep Learning.

Journal of chemical information and modeling
Accurately predicting small molecule partitioning and hydrophobicity is critical in the drug discovery process. There are many heterogeneous chemical environments within a cell and entire human body. For example, drugs must be able to cross the hydro...

Discrimination power of knowledge-based potential dictated by the dominant energies in native protein structures.

Amino acids
Extracting a well-designed energy function is important for protein structure evaluation. Knowledge-based potential functions are one type of the energy functions which can be obtained from known protein structures. The pairwise potential between ato...

In silico minimalist approach to study 2D HP protein folding into an inhomogeneous space mimicking osmolyte effect: First trial in the search of foldameric backbones.

Bio Systems
We have employed our bioinformatics workbench, named Evolution, a Multi-Agent System based architecture with lattice-bead-models, evolutionary-algorithms, and correlated-networks as inhomogeneous spaces, with different correlation lengths, mimicking ...

The Classifying Autoencoder: Gaining Insight into Amyloid Assembly of Peptides and Proteins.

The journal of physical chemistry. B
Despite the importance of amyloid formation in disease pathology, the understanding of the primary structure?activity relationship for amyloid-forming peptides remains elusive. Here we use a new neural-network based method of analysis: the classifyin...

Prediction of Hemolytic Toxicity for Saponins by Machine-Learning Methods.

Chemical research in toxicology
Saponins are a type of compounds bearing a hydrophobic steroid/triterpenoid moiety and hydrophilic carbohydrate branches. The majority of the saponins demonstrate a broad range of prominent pharmacological activities. Nevertheless, many saponins also...

Discovery of small molecule binders of human FSHR(TMD) with novel structural scaffolds by integrating structural bioinformatics and machine learning algorithms.

Journal of molecular graphics & modelling
BACKGROUND: The activation of follicle stimulating hormone receptor (FSHR) by FSH and the consequent downstream signaling activities are crucial for reproductive health. The role of FSHR in tumor progression as well as osteoporosis advancement has al...

Shape-Based Generative Modeling for de Novo Drug Design.

Journal of chemical information and modeling
In this work, we propose a machine learning approach to generate novel molecules starting from a seed compound, its three-dimensional (3D) shape, and its pharmacophoric features. The pipeline draws inspiration from generative models used in image ana...

Recent Advances in Machine Learning Based Prediction of RNA-protein Interactions.

Protein and peptide letters
The interactions between RNAs and proteins play critical roles in many biological processes. Therefore, characterizing these interactions becomes critical for mechanistic, biomedical, and clinical studies. Many experimental methods can be used to det...

Delimiting the knowledge space and the design space of nanostructured lipid carriers through Artificial Intelligence tools.

International journal of pharmaceutics
Nanostructured lipid carriers (NLC) are biocompatible and biodegradable nanoscale systems with extensive application for controlled drug release. However, the development of optimal nanosystems along with a reproducible manufacturing process is still...

Protein Solvent-Accessibility Prediction by a Stacked Deep Bidirectional Recurrent Neural Network.

Biomolecules
Residue solvent accessibility is closely related to the spatial arrangement and packing of residues. Predicting the solvent accessibility of a protein is an important step to understand its structure and function. In this work, we present a deep lear...