Deep learning for computational chemistry
The rise and fall of artificial neural networks is well documented in the scientific literature of
both computer science and computational chemistry. Yet almost two decades later, we are …
both computer science and computational chemistry. Yet almost two decades later, we are …
Progress and challenges for the machine learning-based design of fit-for-purpose monoclonal antibodies
Although the therapeutic efficacy and commercial success of monoclonal antibodies (mAbs)
are tremendous, the design and discovery of new candidates remain a time and cost …
are tremendous, the design and discovery of new candidates remain a time and cost …
Improved protein structure refinement guided by deep learning based accuracy estimation
We develop a deep learning framework (DeepAccNet) that estimates per-residue accuracy
and residue-residue distance signed error in protein models and uses these predictions to …
and residue-residue distance signed error in protein models and uses these predictions to …
[HTML][HTML] Protein–protein interaction prediction with deep learning: A comprehensive review
Most proteins perform their biological function by interacting with themselves or other
molecules. Thus, one may obtain biological insights into protein functions, disease …
molecules. Thus, one may obtain biological insights into protein functions, disease …
Improving prediction of secondary structure, local backbone angles and solvent accessible surface area of proteins by iterative deep learning
Direct prediction of protein structure from sequence is a challenging problem. An effective
approach is to break it up into independent sub-problems. These sub-problems such as …
approach is to break it up into independent sub-problems. These sub-problems such as …
Improving protein disorder prediction by deep bidirectional long short-term memory recurrent neural networks
Motivation Capturing long-range interactions between structural but not sequence neighbors
of proteins is a long-standing challenging problem in bioinformatics. Recently, long short …
of proteins is a long-standing challenging problem in bioinformatics. Recently, long short …
IDP-Seq2Seq: identification of intrinsically disordered regions based on sequence to sequence learning
YJ Tang, YH Pang, B Liu - Bioinformatics, 2020 - academic.oup.com
Motivation Related to many important biological functions, intrinsically disordered regions
(IDRs) are widely distributed in proteins. Accurate prediction of IDRs is critical for the protein …
(IDRs) are widely distributed in proteins. Accurate prediction of IDRs is critical for the protein …
A Deep Learning Network Approach to ab initio Protein Secondary Structure Prediction
Ab initio protein secondary structure (SS) predictions are utilized to generate tertiary
structure predictions, which are increasingly demanded due to the rapid discovery of …
structure predictions, which are increasingly demanded due to the rapid discovery of …
Protein structure prediction using Rosetta
Publisher Summary This chapter elaborates protein structure prediction using Rosetta.
Double-blind assessments of protein structure prediction methods have indicated that the …
Double-blind assessments of protein structure prediction methods have indicated that the …
Improving prediction of protein secondary structure, backbone angles, solvent accessibility and contact numbers by using predicted contact maps and an ensemble of …
Motivation Sequence-based prediction of one dimensional structural properties of proteins
has been a long-standing subproblem of protein structure prediction. Recently, prediction …
has been a long-standing subproblem of protein structure prediction. Recently, prediction …