Machine Learning In Drug Discovery Research Articles

Roche in discovery pact with Recursion

Recursion, a company that uses machine learning for drug discovery, is partnering with Roche and its subsidiary Genentech to identify novel drug targets and develop drugs against them. The firms will rely heavily on single-cell data for neuroscience and cancer indications. The partnership could include up to 40 drug programs. Roche will pay Recursion $150 million up front and potentially more than $300 million in milestone payments for each program.

Application of Artificial Intelligence and Machine Learning in Drug Discovery.

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 information and ultimately to knowledge. Machine Learning or AI is not really new but over last few years, application of better methods have emerged and they have been successfully applied for drug discovery and development. This chapter would provide an overview of these methods and how they have been applied across various work streams, e.g., generative chemistry, ADMET prediction, retrosynthetic analysis, etc. within drug discovery process. This chapter would also attempt to provide caution and pit falls in utilizing these methods blindly while summarizing challenges and limitations.

Leveraging machine learning across diverse fields: Innovations and applications

Machine learning (ML) stands at the frontier of technological advancement across various domains, exhibiting both novel applications and significant enhancements to existing systems. This paper explores the integration of ML in diverse fields, including physics, customer service, geosciences, drug discovery, and smart systems, detailing how these innovations are redefining the capabilities of each sector. In physics, ML has paved the way for new methods such as symbolic regression, which have revolutionized theoretical understanding and experimental applications. In the realm of customer service, AI-driven chatbots have transformed user interactions, offering both improved compliance with user needs and enhanced service quality. Geosciences have benefited from ML in remote sensing and environmental monitoring, where predictive models and data analytics have led to more accurate forecasting and resource management. Furthermore, the integration of ML in drug discovery has accelerated the identification of novel compounds and streamlined the development of new medications, significantly reducing both the time and cost associated with traditional methods. In smart systems, particularly those utilizing Internet of Things (IoT) and 5G technologies, ML has been instrumental in advancing automation and connectivity, thereby enhancing system efficiency and effectiveness. This paper will delve into the specific ML techniques employed in these fields, analyze their impacts, and discuss the potential future directions of ML applications. By providing a comprehensive review of ML frameworks and addressing the associated challenges and ethical considerations, the paper aims to present a holistic view of the pervasive influence of ML across varied disciplines.

New Perspectives on Machine Learning in Drug Discovery.

Artificial intelligence methods, in particular, machine learning, has been playing a pivotal role in drug development, from structural design to the clinical trial. This approach is harnessing the impact of computer-aided drug discovery due to large available data sets for drug candidates and its new and complex manner of information interpretation to identify patterns for the study scope. In the present review, recent applications related to drug discovery and therapies are assessed, and limitations and future perspectives are analyzed.

Machine Learning in Drug Discovery: A Review.

This review provides the feasible literature on drug discovery through ML tools and techniques that are enforced in every phase of drug development to accelerate the research process and deduce the risk and expenditure in clinical trials. Machine learning techniques improve the decision-making in pharmaceutical data across various applications like QSAR analysis, hit discoveries, de novo drug architectures to retrieve accurate outcomes. Target validation, prognostic biomarkers, digital pathology are considered under problem statements in this review. ML challenges must be applicable for the main cause of inadequacy in interpretability outcomes that may restrict the applications in drug discovery. In clinical trials, absolute and methodological data must be generated to tackle many puzzles in validating ML techniques, improving decision-making, promoting awareness in ML approaches, and deducing risk failures in drug discovery.

Feature importance correlation from machine learning indicates functional relationships between proteins and similar compound binding characteristics

Machine learning is widely applied in drug discovery research to predict molecular properties and aid in the identification of active compounds. Herein, we introduce a new approach that uses model-internal information from compound activity predictions to uncover relationships between target proteins. On the basis of a large-scale analysis generating and comparing machine learning models for more than 200 proteins, feature importance correlation analysis is shown to detect similar compound binding characteristics. Furthermore, rather unexpectedly, the analysis also reveals functional relationships between proteins that are independent of active compounds and binding characteristics. Feature importance correlation analysis does not depend on specific representations, algorithms, or metrics and is generally applicable as long as predictive models can be derived. Moreover, the approach does not require or involve explainable or interpretable machine learning, but only access to feature weights or importance values. On the basis of our findings, the approach represents a new facet of machine learning in drug discovery with potential for practical applications.

Artificial Intelligence in Drug Design: Algorithms, Applications, Challenges and Ethics

The discovery paradigm of drugs is rapidly growing due to advances in machine learning (ML) and artificial intelligence (AI). This review covers myriad faces of AI and ML in drug design. There is a plethora of AI algorithms, the most common of which are summarized in this review. In addition, AI is fraught with challenges that are highlighted along with plausible solutions to them. Examples are provided to illustrate the use of AI and ML in drug discovery and in predicting drug properties such as binding affinities and interactions, solubility, toxicology, blood–brain barrier permeability and chemical properties. The review also includes examples depicting the implementation of AI and ML in tackling intractable diseases such as COVID-19, cancer and Alzheimer’s disease. Ethical considerations and future perspectives of AI are also covered in this review.

The significance of the original characteristics of the active substance of injectable chondroitin sulfate preparations for intramuscular injections in the evidence-based medicine

Given the data from normative control resources and the research and development information system of the Ministry of Science and Education of the Russian Federation, we were unable to review the results and the quality of publications comprising method development and analytical validation of quantitative and qualitative values of injectable chondroitin sulfate preparations for intramuscular injections.
 Therefore, the results obtained in the research The development of a method for determining the intrinsic viscosity of an injectable chondroitin sodium sulfate preparation for intramuscular injections of 100 mg/ml, 2 ml remain valid, the method is considered reliable and practical.
 The materials and methods used in the research The development of a method for determining the intrinsic viscosity of an injectable chondroitin sulfate preparation have provided the possibility to determine the intrinsic viscosity for injectable chondroitin sodium sulfate preparations for intramuscular injections and the use of 0.2 M sodium chloride solution as a solvent. Also, the viscosity of the tested solutions in the concentration range of chondroitin sulfate 4.5-20 mg/ml was accurately сalculated, the value of the intrinsic viscosity determined for each of the preparations under test was in the range of 0.03-0.042 m3/kg.
 This study presents the results of a review of recent information, published in a number of academic journals, concerning modern approaches to the treatment and major clinical problems when applying chondroprotectors for arthrological diseases treatment.
 Considering the continuing interest in chondroprotectors, stability problems with formulations and modern possibilities in the application of machine learning in drug discovery, additional pharmaceutical design research (Drug design) is expected - from the docking stage to the quantum calculations stage.

Data Integration Using Advances in Machine Learning in Drug Discovery and Molecular Biology.

While the term artificial intelligence and the concept of deep learning are not new, recent advances in high-performance computing, the availability of large annotated data sets required for training, and novel frameworks for implementing deep neural networks have led to an unprecedented acceleration of the field of molecular (network) biology and pharmacogenomics. The need to align biological data to innovative machine learning has stimulated developments in both data integration (fusion) and knowledge representation, in the form of heterogeneous, multiplex, and biological networks or graphs. In this chapter we briefly introduce several popular neural network architectures used in deep learning, namely, the fully connected deep neural network, recurrent neural network, convolutional neural network, and the autoencoder. Deep learning predictors, classifiers, and generators utilized in modern feature extraction may well assist interpretability and thus imbue AI tools with increased explication, potentially adding insights and advancements in novel chemistry and biology discovery.The capability of learning representations from structures directly without using any predefined structure descriptor is an important feature distinguishing deep learning from other machine learning methods and makes the traditional feature selection and reduction procedures unnecessary. In this chapter we briefly show how these technologies are applied for data integration (fusion) and analysis in drug discovery research covering these areas: (1) application of convolutional neural networks to predict ligand-protein interactions; (2) application of deep learning in compound property and activity prediction; (3) de novo design through deep learning. We also: (1) discuss some aspects of future development of deep learning in drug discovery/chemistry; (2) provide references to published information; (3) provide recently advocated recommendations on using artificial intelligence and deep learning in -omics research and drug discovery.

Imputation Versus Prediction: Applications in Machine Learning for Drug Discovery

Imputation is a powerful statistical method that is distinct from the predictive modelling techniques more commonly used in drug discovery. Imputation uses sparse experimental data in an incomplete dataset to predict missing values by leveraging correlations between experimental assays. This contrasts with quantitative structure–activity relationship methods that use only descriptor – assay correlations. We summarize three recent imputation strategies – heterogeneous deep imputation, assay profile methods and matrix factorization – and compare these with quantitative structure–activity relationship methods, including deep learning, in drug discovery settings. We comment on the value added by imputation methods when used in an ongoing project and find that imputation produces stronger models, earlier in the project, over activity and absorption, distribution, metabolism and elimination end points.

Machine Learning in Drug Discovery and Development Part 1: A Primer.

Artificial intelligence, in particular machine learning (ML), has emerged as a key promising pillar to overcome the high failure rate in drug development. Here, we present a primer on the ML algorithms most commonly used in drug discovery and development. We also list possible data sources, describe good practices for ML model development and validation, and share a reproducible example. A companion article will summarize applications of ML in drug discovery, drug development, and postapproval phase.

Applications of Machine Learning in Drug Discovery

Mingbo Zhang1*, Huipu Han1, Zhili Xu1 and Ming Chu2* Author Affiliations 1College of Pharmacy, PR China 2Department of Immunology, PR China Received: November 11, 2019 | Published: November 18, 2019 Corresponding author: Mingbo Zhang, College of Pharmacy, Liaoning University of Traditional Chinese Medicine, PR China DOI: 10.26717/BJSTR.2019.23.003831

Cover Image, Volume 9, Issue 6

The cover image is based on the Advanced Review Formatting biological big data for modern machine learning in drug discovery, by Miquel Duran‐Frigola, Adrià Fernández‐Torras, Martino Bertoni and Patrick Aloy. https://doi.org/10.1002/wcms.1408 image

Applications of machine learning in drug discovery and development.

Drug discovery and development pipelines are long, complex and depend on numerous factors. Machine learning (ML) approaches provide a set of tools that can improve discovery and decision making for well-specified questions with abundant, high-quality data. Opportunities to apply ML occur in all stages of drug discovery. Examples include target validation, identification of prognostic biomarkers and analysis of digital pathology data in clinical trials. Applications have ranged in context and methodology, with some approaches yielding accurate predictions and insights. The challenges of applying ML lie primarily with thelack of interpretability and repeatability of ML-generated results, which may limit their application. In all areas, systematic and comprehensive high-dimensional data still need to be generated. With ongoing efforts to tackle these issues, as well as increasing awareness of the factors needed to validate ML approaches, the application of ML can promote data-driven decision making and has the potential to speed up the process and reduce failure rates in drug discovery and development.

Machine Learning in Drug Discovery.

Machine Learning in Drug Discovery.

Formatting biological big data for modern machine learning in drug discovery

Biological data is accumulating at an unprecedented rate, escalating the role of data‐driven methods in computational drug discovery. This scenario is favored by recent advances in machine learning algorithms, which are optimized for huge datasets and consistently beat the predictive performance of previous art, rapidly approaching human expert reasoning. The urge to couple biological data to cutting‐edge machine learning has spurred developments in data integration and knowledge representation, especially in the form of heterogeneous, multiplex and semantically‐rich biological networks. Today, thanks to the propitious rise in knowledge embedding techniques, these large and complex biological networks can be converted to a vector format that suits the majority of machine learning implementations. Here, we explain why this can be particularly transformative for drug discovery where, for decades, customary chemoinformatics methods have employed vector descriptors of compound structures as the standard input of their prediction tasks. A common vector format to represent biology and chemistry may push biological information into most of the existing steps of the drug discovery pipeline, boosting the accuracy of predictions and uncovering connections between small molecules and other biological entities such as targets or diseases.This article is categorized under: Computer and Information Science > Databases and Expert Systems Computer and Information Science > Chemoinformatics

PotentialNet for Molecular Property Prediction.

The arc of drug discovery entails a multiparameter optimization problem spanning vast length scales. The key parameters range from solubility (angstroms) to protein–ligand binding (nanometers) to in vivo toxicity (meters). Through feature learning—instead of feature engineering—deep neural networks promise to outperform both traditional physics-based and knowledge-based machine learning models for predicting molecular properties pertinent to drug discovery. To this end, we present the PotentialNet family of graph convolutions. These models are specifically designed for and achieve state-of-the-art performance for protein–ligand binding affinity. We further validate these deep neural networks by setting new standards of performance in several ligand-based tasks. In parallel, we introduce a new metric, the Regression Enrichment Factor EFχ(R), to measure the early enrichment of computational models for chemical data. Finally, we introduce a cross-validation strategy based on structural homology clustering that can more accurately measure model generalizability, which crucially distinguishes the aims of machine learning for drug discovery from standard machine learning tasks.

Virtual Screening of Drug Likeness using Tree Based Ensemble Classifier

In earlier years, the Drug discovery process took years to identify and process a Drug. It takes a normal of 12 years for a Drug to travel from the research lab to the patient. With the introduction of Machine Learning in Drug discovery, the whole process turned out to be simple. The utilization of computational tools in the early stages of Drug development has expanded in recent decades. A computational procedure carried out in Drug discovery process is Virtual Screening (VS). VS are used to identify the compounds which can bind to a Drug target. The preliminary process before analyzing the bonding of ligand and drug protein target is the prediction of drug likeness of compounds. The main objective of this study is to predict Drug likeness properties of Drug compounds based on molecular descriptor information using Tree based ensembles. In this study, many classification algorithms are analyzed and the accuracy for the prediction of drug likeness is calculated. The study shows that accuracy of rotation forest outperforms the accuracy of other classification algorithms in the prediction of drug likeness of chemical compounds. The measured accuracies of the Rotation Forest, Random Forest, Support Vector Machines, KNN, Decision Tree and Naïve Bayes are 98%, 97%, 94.8%, 92.8%, 91.4%, 89.5% respectively.

Cheminformatics in Drug Discovery, an Industrial Perspective.

Cheminformatics has established itself as a core discipline within large scale drug discovery operations. It would be impossible to handle the amount of data generated today in a small molecule drug discovery project without persons skilled in cheminformatics. In addition, due to increased emphasis on "Big Data", machine learning and artificial intelligence, not only in the society in general, but also in drug discovery, it is expected that the cheminformatics field will be even more important in the future. Traditional areas like virtual screening, library design and high-throughput screening analysis are highlighted in this review. Applying machine learning in drug discovery is an area that has become very important. Applications of machine learning in early drug discovery has been extended from predicting ADME properties and target activity to tasks like de novo molecular design and prediction of chemical reactions.

Machine learning in drug discovery and development

AbstractIn‐silico tools and computational techniques have become an integral part of pharmaceutical research. These techniques are now used extensively at various phases during the drug development process. Such tools perform a wide range of functions from retrieving and analyzing data to sophisticated computational models for many biological and chemical processes in drug discovery. Among them, machine‐learning techniques are becoming especially popular because of their emphasis on obtaining accurate predictions. In this overview, we discuss the increasing role of Machine Learning in Drug Discovery. Since this field has close connections with the field of Statistics, we will first describe similarities and differences between these fields. We will then highlight various domains and problems within drug discovery that are utilizing machine‐learning technology to improve and speed up the drug discovery process. Drug Dev Res 72: 112–119, 2011. © 2010 Wiley‐Liss, Inc.