AIMC Topic: Drug Discovery

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Machine Learning for In Silico ADMET Prediction.

Methods in molecular biology (Clifton, N.J.)
ADMET (absorption, distribution, metabolism, excretion, and toxicity) describes a drug molecule's pharmacokinetics and pharmacodynamics properties. ADMET profile of a bioactive compound can impact its efficacy and safety. Moreover, efficacy and safet...

Artificial Intelligence-Enabled De Novo Design of Novel Compounds that Are Synthesizable.

Methods in molecular biology (Clifton, N.J.)
Development of computer-aided de novo design methods to discover novel compounds in a speedy manner to treat human diseases has been of interest to drug discovery scientists for the past three decades. In the beginning, the efforts were mostly concen...

Deep Learning in Structure-Based Drug Design.

Methods in molecular biology (Clifton, N.J.)
Computational methods play an increasingly important role in drug discovery. Structure-based drug design (SBDD), in particular, includes techniques that take into account the structure of the macromolecular target to predict compounds that are likely...

Deep Neural Networks for QSAR.

Methods in molecular biology (Clifton, N.J.)
Quantitative structure-activity relationship (QSAR) models are routinely applied computational tools in the drug discovery process. QSAR models are regression or classification models that predict the biological activities of molecules based on the f...

Has Artificial Intelligence Impacted Drug Discovery?

Methods in molecular biology (Clifton, N.J.)
Artificial intelligence (AI) tools find increasing application in drug discovery supporting every stage of the Design-Make-Test-Analyse (DMTA) cycle. The main focus of this chapter is the application in molecular generation with the aid of deep neura...

Application of Artificial Intelligence and Machine Learning in Drug Discovery.

Methods in molecular biology (Clifton, N.J.)
Machine Learning (ML) and Deep Learning (DL) are two subclasses of Artificial Intelligence (AI), that, in this day and age of big data provides significant opportunities to pharmaceutical discovery research and development by translating data to info...

Fighting COVID-19 with Artificial Intelligence.

Methods in molecular biology (Clifton, N.J.)
The development of vaccines for the treatment of COVID-19 is paving the way for new hope. Despite this, the risk of the virus mutating into a vaccine-resistant variant still persists. As a result, the demand of efficacious drugs to treat COVID-19 is ...

Machine Learning Applied to the Modeling of Pharmacological and ADMET Endpoints.

Methods in molecular biology (Clifton, N.J.)
The well-known concept of quantitative structure-activity relationships (QSAR) has been gaining significant interest in the recent years. Data, descriptors, and algorithms are the main pillars to build useful models that support more efficient drug d...

The potential applications of artificial intelligence in drug discovery and development.

Physiological research
Development of a new dug is a very lengthy and highly expensive process since only preclinical, pharmacokinetic, pharmacodynamic and toxicological studies include a multiple of in silico, in vitro, in vivo experimentations that traditionally last sev...

PreTP-EL: prediction of therapeutic peptides based on ensemble learning.

Briefings in bioinformatics
Therapeutic peptides are important for understanding the correlation between peptides and their therapeutic diagnostic potential. The therapeutic peptides can be further divided into different types based on therapeutic function sharing different cha...