Integration of AI and traditional medicine in drug discovery

Khan, Sumsullah; Rijjal, Dana Al; Piro, Anthony L.; Wheeler, Michael B.

doi:10.1016/j.drudis.2021.01.008

Cited by 29 publications

(32 citation statements)

References 65 publications

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“…This process takes about 10 years and is accompanied by a huge expenditure of funds. Generally, a new drug needs to go through four stages before putting into the market: drug discovery, pre-clinical research, clinical research, and approval of listing (Khan et al, 2021). De novo drug design (Hartenfeller and Schneider, 2010) through existing computer technology can speed up drug development and save research costs.…”

Section: Introductionmentioning

confidence: 99%

Cross-Adversarial Learning for Molecular Generation in Drug Design

Cui

et al. 2022

Front. Pharmacol.

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Molecular generation is an important but challenging task in drug design, as it requires optimization of chemical compound structures as well as many complex properties. Most of the existing methods use deep learning models to generate molecular representations. However, these methods are faced with the problems of generation validity and semantic information of labels. Considering these challenges, we propose a cross-adversarial learning method for molecular generation, CRAG for short, which integrates both the facticity of VAE-based methods and the diversity of GAN-based methods to further exploit the complex properties of Molecules. To be specific, an adversarially regularized encoder-decoder is used to transform molecules from simplified molecular input linear entry specification (SMILES) into discrete variables. Then, the discrete variables are trained to predict property and generate adversarial samples through projected gradient descent with corresponding labels. Our CRAG is trained using an adversarial pattern. Extensive experiments on two widely used benchmarks have demonstrated the effectiveness of our proposed method on a wide spectrum of metrics. We also utilize a novel metric named Novel/Sample to measure the overall generation effectiveness of models. Therefore, CRAG is promising for AI-based molecular design in various chemical applications.

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Section: Introductionmentioning

confidence: 99%

Cross-Adversarial Learning for Molecular Generation in Drug Design

Cui

et al. 2022

Front. Pharmacol.

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“…Machine learning (ML) based regression techniques are becoming wide spread in many areas of data analysis in the chemical 11,12 and pharmaceutical sector [13][14][15][16] ; they have recently been employed in drug development [17][18][19] , diagnostic 20 , treatment algorithm optimisation 21 , drug repurposing 2,22 and material discovery 23,24 ; however such applications are still quite limited despite being very promising 25,26 . Another application of ML technologies in drug discovery is during compound screening or hit/lead generation and optimization enabling a virtual screening platform that offers a quicker and cheaper alternative to classic testing of large compounds libraries 27,28 ; virtual screening can be generally classified in ligand-based or structure-based 28 .…”

Section: Feasibility and Application Of Machine Learning Enabled Fast...mentioning

confidence: 99%

Feasibility and application of machine learning enabled fast screening of poly-beta-amino-esters for cartilage therapies

Perni

Prokopovich

2022

Sci Rep

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Despite the large prevalence of diseases affecting cartilage (e.g. knee osteoarthritis affecting 16% of population globally), no curative treatments are available because of the limited capacity of drugs to localise in such tissue caused by low vascularisation and electrostatic repulsion. While an effective delivery system is sought, the only option is using high drug doses that can lead to systemic side effects. We introduced poly-beta-amino-esters (PBAEs) to effectively deliver drugs into cartilage tissues. PBAEs are copolymer of amines and di-acrylates further end-capped with other amine; therefore encompassing a very large research space for the identification of optimal candidates. In order to accelerate the screening of all possible PBAEs, the results of a small pool of polymers (n = 90) were used to train a variety of machine learning (ML) methods using only polymers properties available in public libraries or estimated from the chemical structure. Bagged multivariate adaptive regression splines (MARS) returned the best predictive performance and was used on the remaining (n = 3915) possible PBAEs resulting in the recognition of pivotal features; a further round of screening was carried out on PBAEs (n = 150) with small variations of structure of the main candidates from the first round. The refinements of such characteristics enabled the identification of a leading candidate predicted to improve drug uptake > 20 folds over conventional clinical treatment; this uptake improvement was also experimentally confirmed. This work highlights the potential of ML to accelerate biomaterials development by efficiently extracting information from a limited experimental dataset thus allowing patients to benefit earlier from a new technology and at a lower price. Such roadmap could also be applied for other drug/materials development where optimisation would normally be approached through combinatorial chemistry.

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“…Besides, the use of omics technologies like genomics, transcriptomics, proteomics, and metabolomics may offer a deeper understanding of the pharmacological effects, pathways, and mechanisms of action of herbal medicines. Their combination with system biology, along with automation and computational strategies, including artificial intelligence (Khan et al 2021), can accelerate the development of innovative herbal medicines (Thomford et al 2018). Moreover, new strategies for the identification of molecules in complex mixtures and their structure determination are available, resulting from the advances in analytical techniques, in addition to progress in chemometrics, data processing, and machine learning approaches (Goodacre 2019), with or without relations to bioassay screening data (Wolfender et al 2019).…”

Section: Exploring System Biology and Antifragilitymentioning

confidence: 99%

Paving New Roads Towards Biodiversity-Based Drug Development in Brazil: Lessons from the Past and Future Perspectives

Braga

2021

Rev. Bras. Farmacogn.

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Although Brazil gathers two fundamental features to occupy a leading position on the development of biodiversity-based medicines, the largest flora on earth and a broad tradition on the use of medicinal plants, the number of products derived from the national genetic heritage is so far modest, either as single drugs or as herbal medicines. This article highlights some aspects that may have contributed to the low rates of success and proposes new insights for innovation. We initially approach the use of medicinal plants in Brazil, molded by its ethnic diversity, and the development of the local pharmaceutical industry. A discussion of some governmental initiatives to support plant-based drug development is then presented. Employing the economic concept of "middle-income trap," we further propose that Brazil is stuck in a "middle-level science trap," since the increase in the number of scientific publications that launched the country to an intermediate publishing position has not been translated into drug development. Two new approaches to escape from this trap are presented, which may result in innovative drug development. The first is based on the exploitation of the antifragility properties of herbal products aiming to investigate non-canonical pharmacodynamics mechanisms of action, aligned with the concepts of system biology. The second is the manufacture of herbal products based on the circular economy principles, including the use of byproducts for the development of new therapeutical agents. The adoption of these strategies may result in innovative phytomedicines, with global competitiveness.

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Integration of AI and traditional medicine in drug discovery

Cited by 29 publications

References 65 publications

Cross-Adversarial Learning for Molecular Generation in Drug Design

Cross-Adversarial Learning for Molecular Generation in Drug Design

Feasibility and application of machine learning enabled fast screening of poly-beta-amino-esters for cartilage therapies

Paving New Roads Towards Biodiversity-Based Drug Development in Brazil: Lessons from the Past and Future Perspectives

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