AIMC Topic: Models, Molecular

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A Machine Learning Study on the Thermostability Prediction of (R)--Selective Amine Transaminase from Aspergillus terreus.

BioMed research international
Artificial intelligence technologies such as machine learning have been applied to protein engineering, with unique advantages in protein structure, function prediction, catalytic activity, and other issues in recent years. Screening better mutants i...

Deep learning to design nuclear-targeting abiotic miniproteins.

Nature chemistry
There are more amino acid permutations within a 40-residue sequence than atoms on Earth. This vast chemical search space hinders the use of human learning to design functional polymers. Here we show how machine learning enables the de novo design of ...

Comparison of UV- and Raman-based monitoring of the Protein A load phase and evaluation of data fusion by PLS models and CNNs.

Biotechnology and bioengineering
A promising application of Process Analytical Technology to the downstream process of monoclonal antibodiesĀ (mAbs) is the monitoring of the Protein A load phase as its control promises economic benefits. Different spectroscopic techniques have been e...

Protein structure prediction using deep learning distance and hydrogen-bonding restraints in CASP14.

Proteins
In this article, we report 3D structure prediction results by two of our best server groups ("Zhang-Server" and "QUARK") in CASP14. These two servers were built based on the D-I-TASSER and D-QUARK algorithms, which integrated four newly developed com...

Improving protein tertiary structure prediction by deep learning and distance prediction in CASP14.

Proteins
Substantial progresses in protein structure prediction have been made by utilizing deep-learning and residue-residue distance prediction since CASP13. Inspired by the advances, we improve our CASP14 MULTICOM protein structure prediction system by inc...

Logistic matrix factorisation and generative adversarial neural network-based method for predicting drug-target interactions.

Molecular diversity
Identifying drug-target protein association pairs is a prerequisite and a crucial task in drug discovery and development. Numerous computational models, based on different assumptions and algorithms, have been proposed as an alternative to the labori...

Highly accurate protein structure prediction for the human proteome.

Nature
Protein structures can provide invaluable information, both for reasoning about biological processes and for enabling interventions such as structure-based drug development or targeted mutagenesis. After decades of effort, 17% of the total residues i...

Learning the Fastest RNA Folding Path Based on Reinforcement Learning and Monte Carlo Tree Search.

Molecules (Basel, Switzerland)
RNA molecules participate in many important biological processes, and they need to fold into well-defined secondary and tertiary structures to realize their functions. Like the well-known protein folding problem, there is also an RNA folding problem....

Recent trends in artificial intelligence-driven identification and development of anti-neurodegenerative therapeutic agents.

Molecular diversity
Neurological disorders affect various aspects of life. Finding drugs for the central nervous system is a very challenging and complex task due to the involvement of the blood-brain barrier, P-glycoprotein, and the drug's high attrition rates. The ava...