AI Medical Compendium Journal:
Current opinion in structural biology

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Deep learning approaches for de novo drug design: An overview.

Current opinion in structural biology
De novo drug design is the process of generating novel lead compounds with desirable pharmacological and physiochemical properties. The application of deep learning (DL) in de novo drug design has become a hot topic, and many DL-based approaches have...

Recent developments in multiscale free energy simulations.

Current opinion in structural biology
Physics-based free energy simulations enable the rigorous calculation of properties, such as conformational equilibria, solvation or binding free energies. While historically most applications have occurred at the atomistic level of resolution, a ran...

Advances in machine learning for directed evolution.

Current opinion in structural biology
Machine learning (ML) can expedite directed evolution by allowing researchers to move expensive experimental screens in silico. Gathering sequence-function data for training ML models, however, can still be costly. In contrast, raw protein sequence d...

Deep learning techniques have significantly impacted protein structure prediction and protein design.

Current opinion in structural biology
Protein structure prediction and design can be regarded as two inverse processes governed by the same folding principle. Although progress remained stagnant over the past two decades, the recent application of deep neural networks to spatial constrai...

Generative deep learning for macromolecular structure and dynamics.

Current opinion in structural biology
Much scientific enquiry across disciplines is founded upon a mechanistic treatment of dynamic systems that ties form to function. A highly visible instance of this is in molecular biology, where characterizing macromolecular structure and dynamics is...

Machine learning approaches for analyzing and enhancing molecular dynamics simulations.

Current opinion in structural biology
Molecular dynamics (MD) has become a powerful tool for studying biophysical systems, due to increasing computational power and availability of software. Although MD has made many contributions to better understanding these complex biophysical systems...

Machine learning for protein folding and dynamics.

Current opinion in structural biology
Many aspects of the study of protein folding and dynamics have been affected by the recent advances in machine learning. Methods for the prediction of protein structures from their sequences are now heavily based on machine learning tools. The way si...

Applications of machine learning in GPCR bioactive ligand discovery.

Current opinion in structural biology
GPCRs constitute the largest druggable family having targets for 475 Food and Drug Administration (FDA) approved drugs. As GPCRs are of great interest to pharmaceutical industry, enormous efforts are being expended to find relevant and potent GPCR li...

Automated discovery of GPCR bioactive ligands.

Current opinion in structural biology
While G-protein-coupled receptors (GPCRs) constitute the largest class of membrane proteins, structures and endogenous ligands of a large portion of GPCRs remain unknown. Because of the involvement of GPCRs in various signaling pathways and physiolog...

Simulations meet machine learning in structural biology.

Current opinion in structural biology
Classical molecular dynamics (MD) simulations will be able to reach sampling in the second timescale within five years, producing petabytes of simulation data at current force field accuracy. Notwithstanding this, MD will still be in the regime of lo...