Machine Learning In Drug Discovery Research Articles

Federated learning for predicting compound mechanism of action based on image-data from cell painting

Having access to sufficient data is essential in order to train accurate machine learning models, but much data is not publicly available. In drug discovery this is particularly evident, as much data is withheld at pharmaceutical companies for various reasons. Federated Learning (FL) aims at training a joint model between multiple parties but without disclosing data between the parties. In this work, we leverage Federated Learning to predict compound Mechanism of Action (MoA) using fluorescence image data from cell painting. Our study evaluates the effectiveness and efficiency of FL, comparing to non-collaborative and data-sharing collaborative learning in diverse scenarios. Specifically, we investigate the impact of data heterogeneity across participants on MoA prediction, an essential concern in real-life applications of FL, and demonstrate the benefits for all involved parties. This work highlights the potential of federated learning in multi-institutional collaborative machine learning for drug discovery and assessment of chemicals, offering a promising avenue to overcome data-sharing constraints.

Revolutionizing Drug Discovery: Harnessing Machine Learning Algorithms

Drug discovery is a crucial element of biomedical research, with the goal of finding and creating new medical treatments for a variety of illnesses. Yet, the conventional process of finding new drugs is frequently impeded by its intrinsic difficulties, such as expensive expenses, long durations, and poor success rates in trials with patients. Recently, the incorporation of machine learning (ML) algorithms has become a revolutionary method to streamline and improve different phases of drug discovery. This summary offers a glimpse into the rapidly growing area of drug discovery using machine learning algorithms, emphasizing its potential to transform the process of developing treatments. The usual process of discovering drugs involves various stages such as identifying the target, finding lead compounds, conducting preclinical tests, undergoing clinical trials, and obtaining regulatory approval. All these phases require a lot of labor, time, and resources, leading to high attrition rates and limited success in turning potential compounds into approved therapies. Nevertheless, researchers can enhance and speed up crucial parts of the drug discovery process by using ML algorithms. ML algorithms use data to aid in drug discovery by utilizing computational models to examine large quantities of biological, chemical, and clinical data. These algorithms can learn from various types of data, such as genomic data, chemical structures, protein interactions, and clinical outcomes, to discover hidden patterns, find new targets for drugs, and forecast the effectiveness and safety of potential treatments. Moreover, machine learning algorithms allow for the investigation of intricate connections between molecular structures and biological effects, making it easier to create improved drug candidates with better effectiveness and specificity. Important uses of machine learning in pharmaceutical research involve finding and confirming targets, screening compounds and improving leads, repurposing drugs, and tailoring treatments for individuals. Commonly used for classification and regression tasks, supervised learning algorithms like support vector machines and random forests predict compound activity, toxicity, and pharmacokinetic properties. Clustering and dimensionality reduction techniques utilized in unsupervised learning algorithms help analyze vast datasets and discover new drug-target interactions. Advanced abilities for analyzing molecular structures, virtual screening, and designing new drugs are provided by deep learning models like convolutional neural networks and recurrent neural networks. Multiple case studies demonstrate how ML algorithms can significantly impact drug discovery. Collaboration among academia, industry, and research institutions has resulted in the creation of new ML-based methods for drug development, identifying targets, and categorizing patients. Nevertheless, there are challenges accompanying the widespread use of ML in drug discovery. In healthcare, it is crucial to address ethical considerations, regulatory hurdles, and data privacy concerns to ensure the responsible and ethical use of ML algorithms. The potential for transforming drug discovery and therapeutic development is immense with the incorporation of machine learning algorithms. Through utilizing data-driven methods, researchers can speed up the discovery of new potential drugs, enhance the effectiveness of treatments, and ultimately enhance the results for patients. Ongoing innovation, teamwork, and cross-disciplinary studies are crucial to fully leverage the potential of ML in revolutionizing drug discovery and precision medicine.

Revolutionizing Cancer Research and Drug Discovery: The Role of Artificial Intelligence and Machine Learning

Abstract: Artificial Intelligence (AI) and Machine Learning (ML) have revolutionized various industries, including cancer research and drug discovery. This article provides a summary of the history of AI and ML, highlighting their resurgence in the 1990s with advancements in computational power and new algorithms. In the context of drug discovery, AI and ML techniques have been applied to accelerate the development of new drugs, from target identification and lead generation to drug repurposing. AI applications in drug design and virtual screening have improved the efficiency of identifying potential drug candidates. Deep learning, a division of ML, has been particularly effective in predicting protein structures and optimizing lead compounds. In anti-cancer drug target prediction, AI and ML algorithms analyze large-scale genomic, proteomic, and clinical data to identify potential targets for cancer therapy. AI has also transformed cancer imaging and diagnosis by enhancing the accuracy and efficiency of cancer detection, classification, and prognosis. Medical imaging analysis, pathology, and radiology have benefited from AI algorithms’ ability to interpret and analyze various imaging modalities. Moreover, AI applications in cancer treatment have facilitated the development of predictive models for treatment response, enabling personalized and targeted therapies based on individual patient characteristics. The purpose of the study was to give facts regarding the integration of artificial intelligence and machine learning in drug discovery and cancer therapy and its significant prospects for improving efficiency, decreasing costs, and improving patient outcomes.

Machine Learning Empowering Drug Discovery: Applications, Opportunities and Challenges.

Drug discovery plays a critical role in advancing human health by developing new medications and treatments to combat diseases. How to accelerate the pace and reduce the costs of new drug discovery has long been a key concern for the pharmaceutical industry. Fortunately, by leveraging advanced algorithms, computational power and biological big data, artificial intelligence (AI) technology, especially machine learning (ML), holds the promise of making the hunt for new drugs more efficient. Recently, the Transformer-based models that have achieved revolutionary breakthroughs in natural language processing have sparked a new era of their applications in drug discovery. Herein, we introduce the latest applications of ML in drug discovery, highlight the potential of advanced Transformer-based ML models, and discuss the future prospects and challenges in the field.

Fingerprinting Interactions between Proteins and Ligands for Facilitating Machine Learning in Drug Discovery.

Molecular recognition is fundamental in biology, underpinning intricate processes through specific protein-ligand interactions. This understanding is pivotal in drug discovery, yet traditional experimental methods face limitations in exploring the vast chemical space. Computational approaches, notably quantitative structure-activity/property relationship analysis, have gained prominence. Molecular fingerprints encode molecular structures and serve as property profiles, which are essential in drug discovery. While two-dimensional (2D) fingerprints are commonly used, three-dimensional (3D) structural interaction fingerprints offer enhanced structural features specific to target proteins. Machine learning models trained on interaction fingerprints enable precise binding prediction. Recent focus has shifted to structure-based predictive modeling, with machine-learning scoring functions excelling due to feature engineering guided by key interactions. Notably, 3D interaction fingerprints are gaining ground due to their robustness. Various structural interaction fingerprints have been developed and used in drug discovery, each with unique capabilities. This review recapitulates the developed structural interaction fingerprints and provides two case studies to illustrate the power of interaction fingerprint-driven machine learning. The first elucidates structure-activity relationships in β2 adrenoceptor ligands, demonstrating the ability to differentiate agonists and antagonists. The second employs a retrosynthesis-based pre-trained molecular representation to predict protein-ligand dissociation rates, offering insights into binding kinetics. Despite remarkable progress, challenges persist in interpreting complex machine learning models built on 3D fingerprints, emphasizing the need for strategies to make predictions interpretable. Binding site plasticity and induced fit effects pose additional complexities. Interaction fingerprints are promising but require continued research to harness their full potential.

Editorial: Artificial intelligence and machine learning for drug discovery, design and repurposing: methods and applications

Editorial: Artificial intelligence and machine learning for drug discovery, design and repurposing: methods and applications

From in silico to in Vitro to In Vivo: Increasing Megakaryocytes to Treat Thrombocytopenia

Hematopoietic stem cells undergo a multi-step differentiation program through several precursor stages to give rise to megakaryocytes (Mk), which ultimately release platelets. Thrombocytopenia can manifest in various clinical settings and can adversely affect a patient's quality of life through easy bruising and risk of bleeding. An example is chemotherapy-induced thrombocytopenia (CIT) which occurs when myelosuppressive chemotherapy is given, especially with certain agents such as platinum analogs and cyclophosphamide. We used a single-cell RNA-sequencing approach to study the differentiation of hematopoietic stem cells to Mk progenitors (MkP) and delineated the trajectory that defines the transition of cells to the megakaryocytic lineage. Using our proprietary platform which pairs new approaches in computation and machine learning in drug discovery, we predicted small molecule interventions that could modulate the hematopoietic lineage direction to increase the differentiation towards MkP and subsequent increases in platelet counts. Using an in vitro 5-day CD34-based Mk differentiation assay we demonstrated that some of our predicted interventions increased generation of MkP from stem cells. Analysis of early MkP (CD34-CD235a-CD71+CD41b+CD42b-) and late MkP (CD34-CD235a-CD71-CD41b+CD42b+) by flow cytometry elucidated differential mechanisms of action such as induction of early or late MKP or both. We performed an 8-day study in healthy mice, which revealed that a selected set of compounds increased MkPs in vivo, consistent with our predicted cell behavior and in vitro findings. To evaluate a possible application for this MkP and platelet increases we tested the efficacy of one of our hit compounds CP3 in a CIT mouse model using Carboplatin. In this model, we recorded a >70 % reduction in platelet counts on day 9 post carboplatin administration, with platelet numbers recovering on day 13 and return to basal levels by day 21. To assess the efficacy of CP3, mice dosed first with this compound were challenged with carboplatin. The primary endpoint of this study was the ability of the compound to rescue mice from nadir of thrombocytopenia and accelerate recovery to baseline. Prior to carboplatin dosing, 7 days of CP3 dosing showed a dose dependent increase in platelet counts confirming our initial studies. However, CP3 dosing did not rescue platelet nadir or accelerate recovery to baseline in the CIT model. This study demonstrated the capacity of CP3 to increase platelets in a healthy BM environment but not under conditions of myelosuppression as seen in the CIT mouse model. Taken together, these findings highlight the benefits of our platform to predict compounds that modulate lineage differentiation towards MkP, and a resultant increase in platelet numbers in an animal model.

Staying Ahead of the Game: How SARS-CoV-2 has Accelerated the Application of Machine Learning in Pandemic Management.

In recent years, machine learning (ML) techniques have garnered considerable interest for their potential use in accelerating the rate of drug discovery. With the emergence of the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) pandemic, the utilization of ML has become even more crucial in the search for effective antiviral medications. The pandemic has presented the scientific community with a unique challenge, and the rapid identification of potential treatments has become an urgent priority. Researchers have been able to accelerate the process of identifying drug candidates, repurposing existing drugs, and designing new compounds with desirable properties using machine learning in drug discovery. To train predictive models, ML techniques in drug discovery rely on the analysis of large datasets, including both experimental and clinical data. These models can be used to predict the biological activities, potential side effects, and interactions with specific target proteins of drug candidates. This strategy has proven to be an effective method for identifying potential coronavirus disease 2019 (COVID-19) and other disease treatments. This paper offers a thorough analysis of the various ML techniques implemented to combat COVID-19, including supervised and unsupervised learning, deep learning, and natural language processing. The paper discusses the impact of these techniques on pandemic drug development, including the identification of potential treatments, the understanding of the disease mechanism, and the creation of effective and safe therapeutics. The lessons learned can be applied to future outbreaks and drug discovery initiatives.

Machine learning in drug discovery

Machine learning in drug discovery

Machine Learning in Drug Discovery

A drug is a substance that when put into the body can change the way the body works and a person's mental state. Discovering the accurate drug plays a vital role in saving precious lives. In traditional drug creation, scientists identify a target in the body and test a large range of chemical compounds on it until they obtain the results. The process gets quicker and more effective with the role of machine learning techniques. This is done by using the huge amount of biological data, medical data, algorithms and statistical models available today. This automation of the drug development process is a key to the current issue of low productivity rate that pharmaceutical companies currently face and helps eliminate the side effects. Machine learning might be a useful tool to further enhance the drug development process.

Application of Artificial Intelligence and Machine Learning in Drug Discovery and Development

Drug discovery has traditionally been a time consuming and expensive endeavor. Additionally, drugs weren’t as effectively designed as those that are being predicted and developed through AI and ML today. Machine learning is a form of artificial intelligence that develops and evolves based on experience (similarly to the human mind), and is more recently being utilized in drug discovery and design. The integration of AI and ML into the drug discovery and development process has allowed for higher target precision, lower toxicity, and better dosage formulations. AI more generally has been introduced to and has been leveraged at, each step of drug development, including target identification and validation, hit identification, as well as hit to lead optimization, and has been key in shortening the previously lengthy drug screening process. AI and ML has also been applied downstream in drug formulation where it has maximized resource utilization and is allowing for web-based 3D printing of drugs. Application of AI in the drug development process has also been extended to the modeling of novel drug-like compounds to predict their ADMET properties. This review will address the stages of drug discovery and development in which the application of AI and ML modeling has altered the traditional development of drugs.
 Keywords: Drug discovery, machine learning, artificial intelligence, computational drug development.

Machine Learning Applications in Drug Discovery and Development

Machine learning has emerged as a powerful tool in the field of drug discovery and development, revolutionizing the way pharmaceutical research is conducted. This abstract provides a concise overview of the key applications and impacts of machine learning in this domain. Drug discovery and development is a complex, time-consuming, and costly process that traditionally relies on trial-and-error experimentation. Machine learning, with its ability to analyze vast datasets and extract meaningful insights, has significantly accelerated and optimized various aspects of this process. One crucial application of machine learning in drug discovery is the prediction of potential drug candidates. Machine learning models can also aid in the identification of biomarkers for diseases, enabling more targeted drug development. In the clinical trial phase, machine learning algorithms assist in patient selection, monitoring, and optimization of trial protocols. Predictive models can identify patient subpopulations most likely to respond to a particular treatment, leading to more efficient trials and better patient outcomes. Additionally, machine learning has streamlined drug repurposing efforts by identifying existing drugs with potential new applications. This approach has the potential to save significant time and resources by leveraging existing safety and efficacy data. Furthermore, machine learning enhances the drug development pipeline by optimizing drug formulation and dosage, predicting adverse reactions, and assisting in regulatory compliance. While machine learning offers tremendous promise in drug discovery and development, it also presents challenges related to data quality, model interpretability, and regulatory approval. Addressing these challenges will be crucial for maximizing the potential of machine learning in the pharmaceutical industry.

DOCKSTRING: Easy Molecular Docking Yields Better Benchmarks for Ligand Design.

The field of machine learning for drug discovery is witnessing an explosion of novel methods. These methods are often benchmarked on simple physicochemical properties such as solubility or general druglikeness, which can be readily computed. However, these properties are poor representatives of objective functions in drug design, mainly because they do not depend on the candidate compound’s interaction with the target. By contrast, molecular docking is a widely applied method in drug discovery to estimate binding affinities. However, docking studies require a significant amount of domain knowledge to set up correctly, which hampers adoption. Here, we present dockstring, a bundle for meaningful and robust comparison of ML models using docking scores. dockstring consists of three components: (1) an open-source Python package for straightforward computation of docking scores, (2) an extensive dataset of docking scores and poses of more than 260,000 molecules for 58 medically relevant targets, and (3) a set of pharmaceutically relevant benchmark tasks such as virtual screening or de novo design of selective kinase inhibitors. The Python package implements a robust ligand and target preparation protocol that allows nonexperts to obtain meaningful docking scores. Our dataset is the first to include docking poses, as well as the first of its size that is a full matrix, thus facilitating experiments in multiobjective optimization and transfer learning. Overall, our results indicate that docking scores are a more realistic evaluation objective than simple physicochemical properties, yielding benchmark tasks that are more challenging and more closely related to real problems in drug discovery.

Limits of Prediction for Machine Learning in Drug Discovery.

In drug discovery, molecules are optimized towards desired properties. In this context, machine learning is used for extrapolation in drug discovery projects. The limits of extrapolation for regression models are known. However, a systematic analysis of the effectiveness of extrapolation in drug discovery has not yet been performed. In response, this study examined the capabilities of six machine learning algorithms to extrapolate from 243 datasets. The response values calculated from the molecules in the datasets were molecular weight, cLogP, and the number of sp3-atoms. Three experimental set ups were chosen for response values. Shuffled data were used for interpolation, whereas data for extrapolation were sorted from high to low values, and the reverse. Extrapolation with sorted data resulted in much larger prediction errors than extrapolation with shuffled data. Additionally, this study demonstrated that linear machine learning methods are preferable for extrapolation.

Application of Machine Learning in Drug Discovery and Development Lifecycle

Machine learning and Artificial Intelligence have significantly advanced in recent years owing to their potential to considerably increase the quality of life while reducing human workload. The paper demonstrates how AI and ML are used in the drug development process to shorten and enhance the overall timeline. It contains pertinent information on a variety of Machine Learning approaches and algorithms that are used across the whole drug development process to speed up research, save expenses, and reduce risks related to clinical trials. A range of QSAR analysis, hit finding, and de novo drug design applications are used in the pharmaceutical industry to enhance decision-making. As technologies like high-throughput screening and computation analysis of databases used for lead and target identification and development create and integrate vast volumes of data, machine learning and deep learning have grown in importance. It has also been emphasized how these cognitive models and tools may be used in lead creation, optimization, and thorough virtual screening. In this paper, problem statements and the corresponding state-of-the-art models have been considered for target validation, prognostic biomarkers, and digital pathology. Machine Learning models play a vital role in the various operations related to clinical trials embracing protocol optimization, participant management, data analysis and storage, clinical trial data verification, and surveillance. Post-development drug monitoring and unique industrially prevalent ML applications of pharmacovigilance have also been discussed. As a result, the goal of this study is to investigate the machine learning and deep learning algorithms utilised across the drug development lifecycle as well as the supporting techniques that have the potential to be useful.

Investigation of Information gain and Chi test feature selection methods in dimensionality reduction using Machine learning for drug discovery

Investigation of Information gain and Chi test feature selection methods in dimensionality reduction using Machine learning for drug discovery

QPoweredCompound2DeNovoDrugPropMax – a novel programmatic tool incorporating deep learning and in silico methods for automated in silico bio-activity discovery for any compound of interest

Network data is composed of nodes and edges. Successful application of machine learning/deep learning algorithms on network data to make node classification and link prediction have been shown in the area of social networks through which highly customized suggestions are offered to social network users. Similarly one can attempt the use of machine learning/deep learning algorithms on biological network data to generate predictions of scientific usefulness. In the presented work, compound-drug target interaction network data set from bindingDB has been used to train deep learning neural network and a multi class classification has been implemented to classify PubChem compound queried by the user into class labels of PBD IDs. This way target interaction prediction for PubChem compounds is carried out using deep learning. The user is required to input the PubChem Compound ID (CID) of the compound the user wishes to gain information about its predicted biological activity and the tool outputs the RCSB PDB IDs of the predicted drug target interaction for the input CID. Further the tool also optimizes the compound of interest of the user toward drug likeness properties through a deep learning based structure optimization protocol. The tool also incorporates a feature to perform automated In Silico modelling to find the interaction between the compounds and the predicted drug targets to uncover their protein-ligand interaction profiles. The program is hosted, supported and maintained at the following GitHub repository. https://github.com/bengeof/Compound2DeNovoDrugPropMax. Anticipating the use of quantum computing and quantum machine learning in drug discovery we use the Penny-lane interface to quantum hardware to turn classical Keras layers used in our machine/deep learning models into a quantum layer and introduce quantum layers into classical models to produce a quantum-classical machine/deep learning hybrid model of our tool and the code corresponding to the same is provided below. https://github.com/bengeof/QPoweredCompound2DeNovoDrugPropMax. HIGHLIGHTS Deep learning based network pharmacology approach to predict the bio-activity of compounds. Further optimization of the compound toward drug like properties using deep learning techniques. Automated in silico modeling and interaction profiling of deep learning predicted target protein-ligand interaction. Communicated by Ramaswamy H. Sarma

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.

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.