AIMC Topic: Biological Products

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Predicting biochemical and physiological effects of natural products from molecular structures using machine learning.

Natural product reports Nov 17, 2021
Covering: 2016 to 2021Discovery of novel natural products has been greatly facilitated by advances in genome sequencing, genome mining and analytical techniques. As a result, the volume of data for natural products has increased over the years, which...
Biological Products Machine Learning Molecular Structure Drug Interactions
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Artificial intelligence-guided discovery of anticancer lead compounds from plants and associated microorganisms.

Trends in cancer Nov 5, 2021
Plants and associated microorganisms are essential sources of natural products against human cancer diseases, partly exemplified by plant-derived anticancer drugs such as Taxol (paclitaxel). Natural products provide diverse mechanisms of action and c...
Machine Learning Drug Discovery Lead Humans Artificial Intelligence Biological Products
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NPClassifier: A Deep Neural Network-Based Structural Classification Tool for Natural Products.

Journal of natural products Oct 18, 2021
Computational approaches such as genome and metabolome mining are becoming essential to natural products (NPs) research. Consequently, a need exists for an automated structure-type classification system to handle the massive amounts of data appearing...
Biological Products Biosynthetic Pathways Neural Networks, Computer
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Design of Biopharmaceutical Formulations Accelerated by Machine Learning.

Molecular pharmaceutics Sep 14, 2021
In addition to activity, successful biological drugs must exhibit a series of suitable developability properties, which depend on both protein sequence and buffer composition. In the context of this high-dimensional optimization problem, advanced alg...
Drug Compounding Nanoparticle Drug Delivery System Bayes Theorem Drug Evaluation, Preclinical Machine Learning Biological Products Humans
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Predicting Antimalarial Activity in Natural Products Using Pretrained Bidirectional Encoder Representations from Transformers.

Journal of chemical information and modeling Aug 16, 2021
Malaria is a threatening disease that has claimed many lives and has a high prevalence rate annually. Through the past decade, there have been many studies to uncover effective antimalarial compounds to combat this disease. Alongside chemically synth...
Antimalarials Support Vector Machine Biological Products Machine Learning Algorithms
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Machine learning models to select potential inhibitors of acetylcholinesterase activity from SistematX: a natural products database.

Molecular diversity Jun 16, 2021
Alzheimer's disease is the most common form of dementia, representing 60-70% of dementia cases. The enzyme acetylcholinesterase (AChE) cleaves the ester bonds in acetylcholine and plays an important role in the termination of acetylcholine activity a...
Workflow Molecular Dynamics Simulation Area Under Curve Models, Molecular Cheminformatics Databases, Pharmaceutical Acetylcholinesterase Cholinesterase Inhibitors Molecular Docking Simulation Drug Discovery Biological Products Structure-Activity Relationship ROC Curve Machine Learning Algorithms Humans
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Active Learning and the Potential of Neural Networks Accelerate Molecular Screening for the Design of a New Molecule Effective against SARS-CoV-2.

BioMed research international May 25, 2021
A global pandemic has emerged following the appearance of the new severe acute respiratory virus whose official name is the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), strongly affecting the health sector as well as the world econom...
Protein Interaction Domains and Motifs Humans Coronavirus 3C Proteases Protein Binding SARS-CoV-2 Biological Products Molecular Docking Simulation Small Molecule Libraries Drug Design Neural Networks, Computer Protein Structure, Secondary Antiviral Agents COVID-19 Drug Treatment Thermodynamics Protease Inhibitors Catalytic Domain Substrate Specificity Gene Expression Kinetics
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Target Prediction Model for Natural Products Using Transfer Learning.

International journal of molecular sciences Apr 28, 2021
A large proportion of lead compounds are derived from natural products. However, most natural products have not been fully tested for their targets. To help resolve this problem, a model using transfer learning was built to predict targets for natura...
Molecular Targeted Therapy Drug Discovery Models, Theoretical Biological Products Deep Learning
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Machine Learning Methods to Predict the Terrestrial and Marine Origin of Natural Products.

Molecular informatics Mar 30, 2021
In recent years there has been a growing interest in studying the differences between the chemical and biological space represented by natural products (NPs) of terrestrial and marine origin. In order to learn more about these two chemical spaces, ma...
Biological Products Machine Learning
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Machine Learning for Biologics: Opportunities for Protein Engineering, Developability, and Formulation.

Trends in pharmacological sciences Jan 23, 2021
Successful biologics must satisfy multiple properties including activity and particular physicochemical features that are globally defined as developability. These multiple properties must be simultaneously optimized in a very broad design space of p...
Artificial Intelligence Machine Learning Humans Protein Engineering Biological Products Proteins
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