AIMC Topic: Drug Discovery
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Application of Machine Learning in Translational Medicine: Current Status and Future Opportunities.
The AAPS journal
May 18, 2021
The exponential increase in our ability to harness multi-dimensional biological and clinical data from experimental to real-world settings has transformed pharmaceutical research and development in recent years, with increasing applications of artifi...
Drug Target Identification with Machine Learning: How to Choose Negative Examples.
International journal of molecular sciences
May 12, 2021
Identification of the protein targets of hit molecules is essential in the drug discovery process. Target prediction with machine learning algorithms can help accelerate this search, limiting the number of required experiments. However, Drug-Target I...
An Integrated Approach toward NanoBRET Tracers for Analysis of GPCR Ligand Engagement.
Molecules (Basel, Switzerland)
May 12, 2021
Gaining insight into the pharmacology of ligand engagement with G-protein coupled receptors (GPCRs) under biologically relevant conditions is vital to both drug discovery and basic research. NanoLuc-based bioluminescence resonance energy transfer (Na...
Big Techs and startups in pharmaceutical R&D - A 2020 perspective on artificial intelligence.
Drug discovery today
May 7, 2021
We investigated what kind of artificial intelligence (AI) technologies are utilized in pharmaceutical research and development (R&D) and which sources of AI-related competencies can be leveraged by pharmaceutical companies. First, we found that machi...
Machine learning techniques applied to the drug design and discovery of new antivirals: a brief look over the past decade.
Expert opinion on drug discovery
May 7, 2021
: Drug design and discovery of new antivirals will always be extremely important in medicinal chemistry, taking into account known and new viral diseases that are yet to come. Although machine learning (ML) have shown to improve predictions on the bi...
Application of machine learning to large in vitro databases to identify drug-cancer cell interactions: azithromycin and KLK6 mutation status.
Oncogene
May 5, 2021
Recent advances in machine learning promise to yield novel insights by interrogation of large datasets ranging from gene expression and mutation data to CRISPR knockouts and drug screens. We combined existing and new algorithms with available experim...
Feature importance of machine learning prediction models shows structurally active part and important physicochemical features in drug design.
Drug metabolism and pharmacokinetics
May 3, 2021
The objective of this study was to obtain the indicators of physicochemical parameters and structurally active sites to design new chemical entities with desirable pharmacokinetic profiles by investigating the process by which machine learning predic...
Improving the therapeutic ratio of radiotherapy against radioresistant cancers: Leveraging on novel artificial intelligence-based approaches for drug combination discovery.
Cancer letters
Apr 29, 2021
Despite numerous advances in cancer radiotherapy, tumor radioresistance remain one of the major challenges limiting treatment efficacy of radiotherapy. Conventional strategies to overcome radioresistance involve understanding the underpinning molecul...
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...
CSConv2d: A 2-D Structural Convolution Neural Network with a Channel and Spatial Attention Mechanism for Protein-Ligand Binding Affinity Prediction.
Biomolecules
Apr 27, 2021
The binding affinity of small molecules to receptor proteins is essential to drug discovery and drug repositioning. Chemical methods are often time-consuming and costly, and models for calculating the binding affinity are imperative. In this study, w...
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