Why 90% of clinical drug development fails and how to improve it?

Acta Pharmaceutica Sinica B, Journal Year: 2022, Volume and Issue: 12(7), P. 3049 - 3062

Published: Feb. 11, 2022

Ninety percent of clinical drug development fails despite implementation many successful strategies, which raised the question whether certain aspects in target validation and optimization are overlooked? Current overly emphasizes potency/specificity using structure‒activity-relationship (SAR) but overlooks tissue exposure/selectivity disease/normal tissues structure‒tissue exposure/selectivity–relationship (STR), may mislead candidate selection impact balance dose/efficacy/toxicity. We propose exposure/selectivity–activity relationship (STAR) to improve optimization, classifies candidates based on drug's potency/selectivity, exposure/selectivity, required dose for balancing efficacy/toxicity. Class I drugs have high specificity/potency needs low achieve superior efficacy/safety with success rate. II requires efficacy toxicity be cautiously evaluated. III relatively (adequate) manageable often overlooked. IV achieves inadequate efficacy/safety, should terminated early. STAR studies development.

Language: Английский

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Molecular contrastive learning of representations via graph neural networks

Nature Machine Intelligence, Journal Year: 2022, Volume and Issue: 4(3), P. 279 - 287

Published: March 3, 2022

Language: Английский

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AI for life: Trends in artificial intelligence for biotechnology

New Biotechnology, Journal Year: 2023, Volume and Issue: 74, P. 16 - 24

Published: Feb. 6, 2023

Due to popular successes (e.g., ChatGPT) Artificial Intelligence (AI) is on everyone's lips today. When advances in biotechnology are combined with AI unprecedented new potential solutions become available. This can help many global problems and contribute important Sustainability Development Goals. Current examples include Food Security, Health Well-being, Clean Water, Energy, Responsible Consumption Production, Climate Action, Life below or protect, restore promote sustainable use of terrestrial ecosystems, sustainably manage forests, combat desertification, halt reverse land degradation biodiversity loss. ubiquitous the life sciences Topics a wide range from machine learning Big Data analytics, knowledge discovery data mining, biomedical ontologies, knowledge-based reasoning, natural language processing, decision support reasoning under uncertainty, temporal spatial representation inference, methodological aspects explainable (XAI) applications biotechnology. In this pre-Editorial paper, we provide an overview open research issues challenges for each topics addressed special issue. Potential authors directly as guideline developing their paper.

Language: Английский

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Advances in De Novo Drug Design: From Conventional to Machine Learning Methods

International Journal of Molecular Sciences, Journal Year: 2021, Volume and Issue: 22(4), P. 1676 - 1676

Published: Feb. 7, 2021

De novo drug design is a computational approach that generates novel molecular structures from atomic building blocks with no priori relationships. Conventional methods include structure-based and ligand-based design, which depend on the properties of active site biological target or its known binders, respectively. Artificial intelligence, including ma-chine learning, an emerging field has positively impacted discovery process. Deep reinforcement learning subdivision machine combines artificial neural networks reinforcement-learning architectures. This method successfully been em-ployed to develop de approaches using variety recurrent networks, convolutional generative adversarial autoencoders. review article summarizes advances in conventional growth algorithms advanced machine-learning methodologies high-lights hot topics for further development.

Language: Английский

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A Comprehensive Discovery Platform for Organophosphorus Ligands for Catalysis

Journal of the American Chemical Society, Journal Year: 2022, Volume and Issue: 144(3), P. 1205 - 1217

Published: Jan. 12, 2022

The design of molecular catalysts typically involves reconciling multiple conflicting property requirements, largely relying on human intuition and local structural searches. However, the vast number potential requires pruning candidate space by efficient prediction with quantitative structure–property relationships. Data-driven workflows embedded in a library can be used to build predictive models for catalyst performance serve as blueprint novel designs. Herein we introduce kraken, discovery platform covering monodentate organophosphorus(III) ligands providing comprehensive physicochemical descriptors based representative conformer ensembles. Using quantum-mechanical methods, calculated 1558 ligands, including commercially available examples, trained machine learning predict properties over 300000 new ligands. We demonstrate application kraken systematically explore organophosphorus how existing data sets catalysis accelerate ligand selection during reaction optimization.

Language: Английский

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Machine Learning in Chemical Engineering: Strengths, Weaknesses, Opportunities, and Threats

Engineering, Journal Year: 2021, Volume and Issue: 7(9), P. 1201 - 1211

Published: July 29, 2021

Chemical engineers rely on models for design, research, and daily decision-making, often with potentially large financial safety implications. Previous efforts a few decades ago to combine artificial intelligence chemical engineering modeling were unable fulfill the expectations. In last five years, increasing availability of data computational resources has led resurgence in machine learning-based research. Many recent have facilitated roll-out learning techniques research field by developing databases, benchmarks, representations applications new frameworks. Machine significant advantages over traditional techniques, including flexibility, accuracy, execution speed. These strengths also come weaknesses, such as lack interpretability these black-box models. The greatest opportunities involve using time-limited real-time optimization planning that require high accuracy can build self-learning ability recognize patterns, learn from data, become more intelligent time. threat today is inappropriate use because most had limited training computer science analysis. Nevertheless, will definitely trustworthy element toolbox engineers.

Language: Английский

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Generative Models as an Emerging Paradigm in the Chemical Sciences

Journal of the American Chemical Society, Journal Year: 2023, Volume and Issue: 145(16), P. 8736 - 8750

Published: April 13, 2023

Traditional computational approaches to design chemical species are limited by the need compute properties for a vast number of candidates, e.g., discriminative modeling. Therefore, inverse methods aim start from desired property and optimize corresponding structure. From machine learning viewpoint, problem can be addressed through so-called generative Mathematically, models defined probability distribution function given molecular or material In contrast, model seeks exploit joint with target characteristics. The overarching idea modeling is implement system that produces novel compounds expected have set features, effectively sidestepping issues found in forward process. this contribution, we overview critically analyze popular algorithms like adversarial networks, variational autoencoders, flow, diffusion models. We highlight key differences between each models, provide insights into recent success stories, discuss outstanding challenges realizing discovered solutions applications.

Language: Английский

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Graph representation learning in biomedicine and healthcare

Nature Biomedical Engineering, Journal Year: 2022, Volume and Issue: 6(12), P. 1353 - 1369

Published: Oct. 31, 2022

Language: Английский

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AI in drug discovery and its clinical relevance

Heliyon, Journal Year: 2023, Volume and Issue: 9(7), P. e17575 - e17575

Published: June 26, 2023

The COVID-19 pandemic has emphasized the need for novel drug discovery process. However, journey from conceptualizing a to its eventual implementation in clinical settings is long, complex, and expensive process, with many potential points of failure. Over past decade, vast growth medical information coincided advances computational hardware (cloud computing, GPUs, TPUs) rise deep learning. Medical data generated large molecular screening profiles, personal health or pathology records, public organizations could benefit analysis by Artificial Intelligence (AI) approaches speed up prevent failures pipeline. We present applications AI at various stages pipelines, including inherently de novo design prediction drug's likely properties. Open-source databases AI-based software tools that facilitate are discussed along their associated problems molecule representation, collection, complexity, labeling, disparities among labels. How contemporary methods, such as graph neural networks, reinforcement learning, models, structure-based (i.e., dynamics simulations docking) can contribute responses also explored. Finally, recent developments investments start-up companies biotechnology, current progress, hopes promotions this article.

Language: Английский

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Deep generative molecular design reshapes drug discovery

Cell Reports Medicine, Journal Year: 2022, Volume and Issue: 3(12), P. 100794 - 100794

Published: Oct. 27, 2022

Recent advances and accomplishments of artificial intelligence (AI) deep generative models have established their usefulness in medicinal applications, especially drug discovery development. To correctly apply AI, the developer user face questions such as which protocols to consider, factors scrutinize, how can integrate relevant disciplines. This review summarizes classical newly developed AI approaches, providing an updated accessible guide broad computational development community. We introduce from different standpoints describe theoretical frameworks for representing chemical biological structures applications. discuss data technical challenges highlight future directions multimodal accelerating discovery.

Language: Английский

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