Artificial Intelligence: A Novel Approach for Drug Discovery

Díaz, Ómar Huertas; Dalton, James A. R.; Giraldo, Jesús

doi:10.1016/j.tips.2019.06.005

Cited by 47 publications

(17 citation statements)

References 10 publications

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“…Recently, AI-based drug-discovery has been much publicised as an effective approach to accelerate the drug discovery process (Alvarez-Machancoses and Fernandez-Martinez 2019; Amino Acid Synthesizer ; Diaz et al 2019;Hervey 2020;Sellwood et al 2018;Yang et al 2019;Zhu 2020). Broadly, AI can be very useful for initial drug discovery in two main ways, one is through screening millions of chemical compounds available in different databases for potential drugs in simulation tests, and the other is to identify novel drugs that can latch onto the targets, to reduce their infectivity.…”

Section: Ai/ml-based Methods In Covid-19 Drug Repurposingmentioning

confidence: 99%

Artificial Intelligence-driven drug repurposing and structural biology for SARS-CoV-2

Prasad

Kumar

2021

Preprint

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It has been said that COVID-19 is a generational challenge in many ways. But, at the same time, it becomes a catalyst for collective action, innovation, and discovery. Realizing the full potential of artificial intelligence (AI) for structure determination of unknown proteins and drug discovery are some of these innovations. Potential applications of AI include predicting the structure of the infectious proteins, identifying drugs that may be effective in targeting these proteins, and proposing new chemical compounds for further testing as potential drugs. AI and machine learning (ML) allow for rapid drug development including repurposing existing drugs. Algorithms were used to search for novel or approved antiviral drugs capable of inhibiting SARS-CoV-2. This paper presents a survey of AI and ML methods being used in various biochemistry of SARS-CoV-2, from structure to drug development, in the fight against the deadly COVID-19 pandemic. It is envisioned that this study will provide AI/ML researchers and the wider community an overview of the current status of AI applications particularly in structural biology, drug repurposing and development and motivate researchers in harnessing AI potentials in the fight against COVID-19.

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Section: Ai/ml-based Methods In Covid-19 Drug Repurposingmentioning

confidence: 99%

Artificial Intelligence-driven drug repurposing and structural biology for SARS-CoV-2

Prasad

Kumar

2021

Preprint

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“…Atom wise is used by Biopharma for the identification and prediction of the small structures in 3D basis now a days. The atomwise has used this technique for the identification of active molecules in the case of Ebola virus [29]. Using Artificial Intelligence Company has developed two different drugs which are more effective against Ebola virus as compared to medications which are existed before.…”

Section: Fig02 General Steps For Drug Discovery Using Ai and MLmentioning

confidence: 99%

Role of Artificial Intelligence and Machine Learning in Bioinformatics: Drug Discovery and Drug Repurposing

Quazi¹

2021

Preprint

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Artificial intelligence AI or machine learning has proven to be a potential activity in the health and biomedical sciences. Previous research it has found that AI can learn new data and transform it into the useful knowledge. In the field of pharmacology, the aim is to design more efficient and novel vaccines using this method which are also cost effective. The underlying fact is to predict the molecular mechanism and structure for increased likelihood of developing new drugs. Clinical, electronic and high resolution imaging datasets can be used as inputs to aid the drug development niche. Moreover, the use of comprehensive target activity has been performed for repurposing a drug molecule by extending target profiles of drugs which also include off targets with therapeutic potential providing a new indication.

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“…Therefore, the high-performance accelerated computation used to generate simulated protein motions for comparison can be effectively partnered with high-performance methods for optimally extracting and learning the underlying dynamic feature differences that define the different functional states of proteins. Although machine learning has recently been applied to individual MD studies for a variety of specific tasks (17)(18)(19), there is no current software platform for the general application of machine learning to general comparative problems in protein dynamics.…”

Section: Introductionmentioning

confidence: 99%

DROIDS 3.0—Detecting Genetic and Drug Class Variant Impact on Conserved Protein Binding Dynamics

Babbitt

Fokoué

Evans

et al. 2020

Biophysical Journal

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The application of statistical methods to comparatively framed questions about the molecular dynamics (MD) of proteins can potentially enable investigations of biomolecular function beyond the current sequence and structural methods in bioinformatics. However, the chaotic behavior in single MD trajectories requires statistical inference that is derived from large ensembles of simulations representing the comparative functional states of a protein under investigation. Meaningful interpretation of such complex forms of big data poses serious challenges to users of MD. Here, we announce Detecting Relative Outlier Impacts from Molecular Dynamic Simulation (DROIDS) 3.0, a method and software package for comparative protein dynamics that includes maxDemon 1.0, a multimethod machine learning application that trains on large ensemble comparisons of concerted protein motions in opposing functional states generated by DROIDS and deploys learned classifications of these states onto newly generated MD simulations. Local canonical correlations in learning patterns generated from independent, yet identically prepared, MD validation runs are used to identify regions of functionally conserved protein dynamics. The subsequent impacts of genetic and/or drug class variants on conserved dynamics can also be analyzed by deploying the classifiers on variant MD simulations and quantifying how often these altered protein systems display opposing functional states. Here, we present several case studies of complex changes in functional protein dynamics caused by temperature, genetic mutation, and binding interactions with nucleic acids and small molecules. We demonstrate that our machine learning algorithm can properly identify regions of functionally conserved dynamics in ubiquitin and TATA-binding protein (TBP). We quantify the impact of genetic variation in TBP and drug class variation targeting the ATP-binding region of Hsp90 on conserved dynamics. We identify regions of conserved dynamics in Hsp90 that connect the ATP binding pocket to other functional regions. We also demonstrate that dynamic impacts of various Hsp90 inhibitors rank accordingly with how closely they mimic natural ATP binding.

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Artificial Intelligence: A Novel Approach for Drug Discovery

Cited by 47 publications

References 10 publications

Artificial Intelligence-driven drug repurposing and structural biology for SARS-CoV-2

Artificial Intelligence-driven drug repurposing and structural biology for SARS-CoV-2

Role of Artificial Intelligence and Machine Learning in Bioinformatics: Drug Discovery and Drug Repurposing

DROIDS 3.0—Detecting Genetic and Drug Class Variant Impact on Conserved Protein Binding Dynamics

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