Deep Learning with Big Data Analytics for Drug Discovery: Enhance Prediction Accuracy and Reduce Attrition

Research Proposal
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How to write a research proposal for a strong PhD application

[1]DOI: 10.1002/ddr.22115 (Artificial intelligence revolutionizing drug development: Exploring opportunities and challenges
[2]WaringMJ,ArrowsmithJ,LeachAR,LeesonPD,MandrellS,etal.2015.Ananalysisoftheattritionof drug candidates from four major pharmaceutical companies. Nat. Rev. Drug Discov. 14:475–86
[3]Chen, W., Liu, X., Zhang, S., & Chen, S. (2023). Artificial intelligence for drug discovery: Resources, methods, and applications. Molecular Therapy—Nucleic Acids, 31, P691–P702. https://doi.org/10.1016/j. omtn.2023.02.019
[4]Jiang, F., Jiang, Y., Zhi, H., Dong, Y., Li, H., Ma, S., Wang, Y., Dong, Q., Shen, H., & Wang, Y. (2017). Artificial intelligence in healthcare: Past, present and future. Stroke and Vascular Neurology, 2(4), 230–243. https://doi.org/10.1136/svn-2017-000101
[5]Wagner J, Dahlem AM, Hudson LD, Terry SF, Altman RB, et al. 2018. A dynamic map for learning, communicating, navigating and improving therapeutic development. Nat. Rev. Drug Discov. 17(2):150
[6]Artificial Intelligence for Drug Discovery: Are We There Yet?
[7] Williams, D. A., & Lemke, T. L. (2012). Foye's Principles of Medicinal Chemistry. Philadelphia: Lippincott Williams & Wilkins.
[8]Bittner, M.‐I., & Farajnia, S. (2022). AI in drug discovery: Applications, opportunities, and challenges. Patterns, 3(6), 100529. https://doi. org/10.1016/j.patter.2022.100529
[9]Rodgers, T.‐H., & Buchanan, I. R. (2023). AI/ML in drug discovery & development: Potential and challenges. https://www.drugdiscovery online.com/doc/ai-ml-in-drug-discovery-development-potential- and-challenges-0001

Abstract

With the increase in chemical or biological data, modern drug discovery has advanced to the big data era for drug candidates. Artificial intelligence approaches, such as machine learning and relevant innovative models, have paved the road to efficiency and safety evaluations of future rational drug development and optimization based on big data analysis.
This research aims to explore the integration of deep learning and big data analytics to improve the drug discovery process. The main question we seek to answer is how these advanced technologies can be combined to enhance prediction accuracy and reduce the high failure rate in developing new drugs. The traditional way of drug discovery is often slow, expensive, and has a high attrition rate. By leveraging deep learning, which is a form of machine learning, and big data analytics, we can analyze large volumes of biological and chemical data more efficiently. This integration promises to identify potential drug candidates with greater accuracy, thereby lowering attrition rates in drug discovery. Our study will address the current challenges in drug discovery, review existing research on the utilities of deep learning and big data analytics in this field, and outline our research objectives and methodology. We expect to make such an achievement that integrating these technologies will provide new solutions for more accurate predictions and fewer failed drug candidates.

keywords: deep learning, big data, drug discovery, QSAR

Background

Drug discovery is the process of finding new medicines to treat diseases, and it involves many procedures, including target identification, lead compounds optimization, efficacy and safety evaluation, preclinical testing, clinical trials, and regulatory approval[1]. This process is complex, time-consuming, expensive, and has a high failure rate, especially during clinical trials[2]. Additionally, researchers must analyze a large amount of complex data, which makes it an even more costly undertaking.

In recent years, there has been a lot of interest in using new AI technologies like deep learning and big data analytics to promote drug discovery. Deep learning, a type of advanced machine learning, has the capability to analyze massive biological data sets to predict which compounds might be effective. Big data analytics can process large datasets quickly, finding patterns that humans might miss. The two technologies can contribute to improving prediction accuracy, speed up data analysis, and optimize lead compounds more accurately. Consequently, combining deep learning with big data analytics can present a potential resolution to the enhancement of success in clinical trials[3].

Despite the significant benefits related to the combination of deep learning with big data analysis for drug discovery, there are still issues that need considerable, like the measurement of data quality, integration of different data sources, interpretability of learning models, and sufficient computational resources[4]. All in all, deep learning with big data analytics provides both potential challenges and promising opportunities in drug discovery. Therefore, it is necessary to conduct this research to overcome these challenges and fully leverage the advantages of these technologies, which eventually lead to prediction accuracy enhancement and attrition reduction in the process.

Literature Review

In this review, we discuss the current research climate of deep learning for drug discovery in the big data area. Firstly, we give an overview of drug discovery primarily focusing on the specific procedure. Then, we clarify the transformation of technology used from initial machine learning to deep learning. Meanwhile, we explore how big data analysis is embedded in the former on drug discovery. Finally, we identify the challenges that need to be faced now. We attempt to argue the future directions of these two technologies applied in enhancing prediction accuracy and reducing attrition. The table of deep learning based drug discovery programs is also added.

Overview of Drug Discovery

Drug discovery is a systematic scientific process that aims to identify, design, and develop novel therapeutic agents to cure, ameliorate, or prevent diseases and medical conditions. Drug discovery is often called a pipeline, which suggests a unidirectional transition from lead to candidate and marketed drug, supported by basic and clinical research [5, 6].

Drug discovery is a very essential, complex and always time-consuming process. It typically takes more than 10 years and requires billions of dollars in expenses. The process mainly has the following key steps:

1. Target Identification: Initially, scientists settle on a target, usually a protein or gene, that is associated with a specific disease. This target is what the new drug will interact with to exert its therapeutic effects.

2. Lead Compound Identification: Next, researchers conduct extensive screening to search for which compounds can affect the target. These compounds from a vast library of candidates are called lead compounds. Scientists test enormous compounds to find the ones with potential efficacy.

3. Lead Optimization: Upon identifying lead compounds, scientists proceed to modify and optimize these compounds. To enhance efficacy and safety to make them more suitable for therapeutic use.

4. Preclinical Testing: The optimized compounds then undergo strict testing in the laboratory and on animals. This phase aims to assess the effectiveness and safety of these compounds before progressing to human trials.

5. Clinical Trials: If preclinical tests yield expected results, the compounds are then conducted on humans in multiple phases of clinical trials. These trials are evaluated to confirm the safety and efficacy of the new drugs.

6. Regulatory Approval: Ultimately, if the clinical trials are successful, the new drug must obtain approval from regulatory authorities. This approval is essential before the drug can be marketed and made available to patients.

Successful drug discovery requires optimizing factors like drug-target interactions, pharmacokinetics, and clinical outcomes, including therapeutic effects and side effects. Pharmacokinetics includes absorption, distribution, metabolism, excretion, and toxicity (ADMET)[7]. The main areas of focus are the disease, the drug target, and the treatment method.

from machine learning to deep learning
In the 1960s, the first Quantitative structure-activity relationship (QSAR) research was published [67]. QSAR models[9], along with other existing computational technologies, have been widely used in the drug development pipeline since then. Before the 1990s, linear regressions were a popular computational method for developing models in the early stages of drug discovery [68].

From the 1990s to the 2000s, new machine learning approaches, based on nonlinear modeling algorithms, were frequently applied to predicting drug-target interactions, drug efficacy, and possible side effects. These algorithms include decision trees, random forests[79,80], support vector machines[78], and others. However, traditional machine learning algorithms often require a lot of feature engineering, where experts manually select and design features from the raw data. This process can be time-consuming and may not capture all the important information. Also, machine learning models can struggle with very large and complex datasets, which are common in drug discovery.

Deep learning, a more advanced form of machine learning, was first introduced with ANNs in the 1980s (4). Deep neural networks (DNNs), also known as deep neural nets, were published in 2015 (103), then the big data notion was presented next year (41, 104). Deep learning uses neural networks with many layers to automatically learn features from the data. This means that deep learning models can process raw data directly and find complex patterns that traditional machine learning might miss. In drug discovery, deep learning can handle large and complex datasets, such as those found in genomics, proteomics, and chemical libraries. Deep learning models have been used to predict drug-target interactions, optimize lead compounds, and identify new drug candidates. Merck[38]supported a QSAR machine learning challenge, and it was the first project where deep learning methods demonstrated much better performance than other machine learning techniques in drug discovery. Another application is in analyzing high-throughput screening (HTS) [24,26]data to identify potential drug candidates more quickly.

big data analytics in drug discovery
The term "big data" refers to a collection of data sets that are difficult to deal with traditional data analysis tools for their complexity[41]. Big data analytics involves processing and analyzing large volumes of data to extract valuable insights. The integration of big data analytics with deep learning has opened new possibilities in drug discovery.  In drug discovery, this means handling diverse types of data, such as genomic sequences, chemical structures, clinical trial results, and patient health records.
In the past ten years, several data-sharing projects started alongside the development of HTS techniques in various screening centers. For instance, PubChem is a public repository that contains chemical structures and their biological properties[44-46]. ChEMBL is a database that has binding, functional, ADME, and toxicity information for many different chemicals, much like PubChem [48]. ChEMBL has a lot more hand vetted data from the literature than PubChem does.
Public big data sources are often identified by the large size of their electronic files. To process and analyze this big data, new hardware techniques like cloud computing[41,51] and graphics processing units (GPUs) [52]are required, instead of personal computers with central processing units.


Opportunities and Challenges

The deep learning models and big data analytics in drug discovery present both significant opportunities and challenges.
These include the requirement for extensive high-quality data to train algorithms, the lack of data standardization and the risk of biases in both data and algorithms. Additionally, regulatory and ethical considerations necessitate transparency in decision‐making and address potential unintended consequences [8,9].
Here we will list the key items requiring attention.

Challenges

1. Data Quality and Integration: One of the biggest challenges is ensuring the quality and availability of data. Drug discovery involves obtaining datasets from multiple sources, including biological experiments, chemical libraries, and clinical trials. These datasets must be accurate, complete, and properly formatted. Poor quality or incomplete data can lead to incorrect predictions and wasted resources. Each of these data types has its own format and structure. Effective data integration is essential to gain a comprehensive understanding of the drug discovery process and to develop accurate predictive models.

2. Interpretability of Deep Learning Models: Deep learning models, while powerful, are often seen as "black boxes"[39] because it is difficult to understand how they make decisions. This absence of transparency is a problem in drug discovery, where understanding the mechanisms of drug action is crucial. Researchers need to develop methods to interpret the predictions made by deep learning models to ensure they are reliable and actionable.

3. Bias in data and algorithms: Big data's issues are characterized as the "four Vs": volume (data scale), velocity (data increase), variety (source diversity), and veracity (data uncertainty) [31, 32]. The data used to train deep learning algorithms can have biases, resulting in skewed predictions. Also, the algorithms themselves might be biased, affecting the accuracy and precision of drug candidates. It is necessary to address and reduce these biases to ensure that corresponding applications in drug development are fair and dependable.

4. Regulatory and Ethical Considerations: The use of deep learning and big data in drug discovery also raises regulatory and ethical concerns. The protection of patient privacy and data security is paramount. Additionally, regulatory bodies need to develop guidelines to ensure the safety and efficacy of new drugs.

Opportunities

1. Improved Prediction Accuracy: By leveraging deep learning and big data analytics, researchers can improve the accuracy of predictions regarding drug-target interactions, drug efficacy, and potential side effects. This can reduce the time and cost associated with drug development and increase the likelihood of success.

2. Personalized Medicine: Big data analytics allows for the analysis of patient-specific data, paving the way for personalized medicine. This approach can identify the most effective treatments for individual patients based on their genetic makeup and medical history, leading to better treatment outcomes and reduced adverse effects.

3. Accelerated Drug Discovery Process: The ability to quickly process and analyze large datasets can significantly speed up the drug discovery process. Deep learning models can rapidly identify promising drug candidates and optimize lead compounds, reducing the time required to bring new drugs to market.

4. Cost Reduction: Implementing deep learning and big data analytics can lower the rate of drug attritions. By improving prediction accuracy and speeding up the process, these technologies can decrease the resources needed for experimental testing and clinical trials, which leads to reducing the overall cost.

Conclusion

 Clinical and pharmaceutical datasets grow rapidly in the current big data age. In the meantime, novel machine learning approaches are also in high demand. Compared to traditional machine learning, the recent deep learning models have shown huge advantages in enhancing efficiency, accuracy, and speed. While there are significant challenges to deep learning and big data analytics in drug discovery, the opportunities offered are substantial. Addressing issues such as data quality and integration, interpretability of models, and ethical considerations will be crucial in fully leveraging the potential of these technologies. Nevertheless, the advancements in deep learning and big data hold the promise of revolutionizing modern drug discovery, making it safer, more efficient, and more effective in developing new treatments for patients.

Research Problem

In light of the above literature review, traditional methods and even earlier applications of machine learning have various limitations, particularly in the aspects of handling large and complex datasets and ensuring prediction accuracy. Despite the corresponding issues, deep learning, combined with big data analytics, offers significant potential to address these challenges by leveraging large volumes of diverse data to improve predictive capabilities and streamline the drug discovery process.

Given these insights, we propose such a research problem: How can deep learning and big data analytics be effectively integrated to enhance prediction accuracy and reduce attrition in the process of drug discovery?

Aims and objectives

Overall Aim
The primary aim of this research is to enhance prediction accuracy and reduce attrition in the process of drug discovery by investigating the combination of deep learning and big data analytics. Broadly, this research seeks to develop methodologies and frameworks that improve the efficiency and success rate of identifying and developing new drugs.

Specific Objectives
To achieve the aim proposed, this research is divided into the following specific objectives:

1. Assess Data Quality and Integration Techniques:

   • Evaluate existing datasets used in drug discovery to identify common issues related to data quality.

   • Investigate and implement effective data integration methods to combine diverse data types (e.g., genomic, chemical, clinical) into a unified format suitable for deep learning analysis.

2. Develop and Optimize Deep Learning Models:

   • Design deep learning models tailored for drug discovery tasks, such as predicting drug-target interactions and optimizing lead compounds.

   • Optimize these models for accuracy and efficiency, using techniques like hyperparameter tuning and advanced neural network architectures.

3. Enhance Model Interpretability:

   • Develop methods to improve the interpretability of deep learning models, ensuring the predictions can be understood and trusted by researchers in the field.

   • Implement visualization tools and techniques to make the decision-making processes of these models more transparent.

4. Evaluate Computational Resource Requirements:

   • Analyze the computational resources required for training and deploying deep learning models on large drug discovery datasets.

   • Identify and implement strategies to optimize computational efficiency, such as parallel computing and cloud-based solutions.

5. Validate Models with Real-World Data:

   • Test the developed models using real-world drug discovery data to assess their practical applicability and performance.

   • Collaborate with pharmaceutical companies or research institutions to validate the models on their datasets and workflows.

6. Address Regulatory and Ethical Considerations:

   • Review current regulatory guidelines and ethical standards related to the use of AI and big data in drug discovery.

   • Develop recommendations to ensure that the research adheres to these standards and addresses potential ethical concerns, such as patient privacy and data security.

Expected contribution

This research is expected to make several significant contributions to the field of drug discovery:

Improved Prediction Accuracy and Reduced Attrition

By integrating deep learning and big data analytics, this research aims to enhance prediction accuracy in drug discovery, identify promising drug candidates more reliably, and reduce attrition rates by finding out potential failures early.

Frameworks for Data Integration and Model Interpretability

The research will develop strategies for integrating diverse data types and creating interpretable deep learning models. This will address the challenges of managing complex datasets and the "black box" problem, making the models' predictions more understandable and trustworthy.

Regulatory and Ethical Compliance

Developing frameworks to ensure regulatory and ethical compliance will address concerns related to patient privacy and data security, facilitating the responsible application of machine learning and big data in drug discovery.

Proposed methodology

This research will explore how to integrate deep learning and big data analytics in drug discovery to improve prediction accuracy and reduce attrition rates. We will use a mixed-methods approach, combining qualitative and quantitative methodologies.

Theoretical Frameworks and Technologies

The proposed methodology draws on theoretical frameworks from machine learning, deep learning, and big data analytics. The integration of these technologies aims to leverage their complementary strengths to resolve the issues in drug discovery.

1. Deep Learning Algorithms: We will use advanced deep learning algorithms, like Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs), to analyze complex biological and chemical data. These algorithms excel at identifying patterns within large datasets.

2. Big Data Analytics: We will use tools for big data processing, such as Apache Hadoop and Apache Spark, to handle the large amounts of data generated in drug discovery. These tools facilitate the efficient storage, retrieval, and processing of data.

Equipment and Materials

1. High-Performance Computing (HPC): We will use high-performance computing resources, including clusters and cloud-based platforms, to manage the computationally intensive tasks of training deep learning models. This infrastructure is necessary for processing large-scale datasets and running complex algorithms.

2. Software Tools: We will use various software tools and programming languages, including:

   • Python: For scripting and model development, using libraries like TensorFlow, and Keras.

   • R: For statistical analysis and data visualization.

   • Jupyter Notebooks: For interactive data analysis and model prototyping.

3. Data Sources: We will need access to diverse datasets, including:

   • Public genomic and chemical databases.

   • Clinical trial data.

   • Proprietary datasets from pharmaceutical collaborations.

Qualitative and Quantitative Methods

To achieve a comprehensive understanding of how deep learning and big data analytics can improve drug discovery, we will use both qualitative and quantitative methods. This mixed-methods approach allows us to gather detailed insights and validate our findings statistically.

Qualitative Methods

Expert Interviews: We will conduct interviews with domain experts in pharmacology, bioinformatics, and machine learning. These interviews will help us understand the current challenges and opportunities in drug discovery from different perspectives. Thematic analysis will be used to analyze the interview data and identify key themes and insights.
Literature Review: We will perform a thorough review of existing literature on deep learning, big data analytics, and drug discovery. This review will help us understand the current state of research, identify gaps, and justify the need for our study.

Quantitative Methods

Data Collection

Data Sources
To conduct this research, we will collect data from various sources to ensure comprehensive coverage of the drug discovery process. We will use public genomic databases, such as The Cancer Genome Atlas (TCGA) and GenBank, which provide crucial genetic information. Additionally, we will access clinical trial data from databases like ClinicalTrials.gov and the European Clinical Trials Database (EudraCT). Collaborations with pharmaceutical companies will allow us to obtain proprietary datasets, including chemical properties of compounds, high-throughput screening (HTS) assay results, and pharmacokinetic and pharmacodynamic data. Lastly, we will review relevant scientific literature from sources like PubMed and Google Scholar to extract additional data and insights.

Collection Methods
We will use a combination of automated data extraction and manual curation to gather data from the identified sources. Automated data extraction will involve using web scraping tools and APIs to efficiently gather large volumes of data from public databases. Manual curation will be necessary for extracting and validating data from proprietary sources and literature to ensure accuracy and relevance. After gathering the data, we will integrate it into a unified database through processes of data cleaning, normalization, and transformation to ensure consistency and compatibility.

data analysis
The data analysis phase will begin with descriptive statistics to summarize the fundamental characteristics of the datasets, such as mean, median, standard deviation, and frequency distributions. This will help us understand the data structure and identify any initial patterns or anomalies. We will use data visualization tools like Python's Matplotlib and Seaborn to create visual representations, facilitating intuitive understanding and effective communication of the findings.

Next, we will apply inferential statistics and develop machine learning models. Regression and correlation analyses will test hypotheses and identify significant drug efficacy and safety predictors. We will employ traditional machine learning algorithms, including linear regression, logistic regression, decision trees, and support vector machines, alongside advanced deep learning models like Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs). These models will be rigorously trained and validated using cross-validation techniques to ensure robustness and generalizability. Performance will be assessed using metrics such as accuracy, precision, recall, and F1-score, ensuring reliable predictions for drug discovery.

Justification of Methodology
The methodology chosen for this research integrates deep learning and big data analytics to enhance drug discovery processes. We intend to explain why the methodology we chose is the most suitable and why other approaches may be less appropriate.

Appropriateness of the Methodology

1. Integration of Deep Learning and Big Data Analytics: The combination of deep learning and big data analytics is highly suitable for drug discovery due to the complexity and volume of biological and chemical data involved. Deep learning algorithms, such as Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs), excel at identifying patterns in large datasets. Big data tools like Apache Hadoop and Apache Spark enable efficient processing and storage of vast amounts of data. Together, they provide a powerful framework for improving prediction accuracy and reducing attrition rates.

2. Mixed-Methods Approach: Using both qualitative and quantitative methods allows for a comprehensive understanding of the research problem. Qualitative methods, such as expert interviews and literature reviews, provide in-depth insights and context. Quantitative methods, including statistical analysis and machine learning model development, enable rigorous testing and validation of hypotheses. This mixed-methods approach ensures that the research is well-rounded and robust.

3. Reproducibility and Validation: The methodology includes thorough validation techniques, such as cross-validation, to ensure the reliability of the models. This is crucial for building trust in the results and ensuring the models can be applied in real-world drug discovery scenarios.

Why Other Approaches Are Less Suitable

1. Traditional Machine Learning Methods: While traditional machine learning methods can be effective, they often struggle with the high dimensionality and complexity of biological and chemical data. Deep learning algorithms, on the other hand, are specifically designed to handle such complexity, making them more suitable for this research.

2. Small-Scale Data Analysis: Approaches that do not leverage big data analytics may be limited by the volume of data they can process. In drug discovery, where large datasets are common, big data tools are essential for managing and analyzing data at scale. Smaller-scale methods may miss important patterns and relationships present in larger datasets.

3. Purely Qualitative or Quantitative Approaches: Relying solely on qualitative or quantitative methods would provide an incomplete picture. Qualitative methods alone may lack the statistical rigor needed to validate findings, while quantitative methods alone may miss the contextual understanding provided by qualitative insights. The mixed-methods approach combines the strengths of both, offering a more comprehensive solution.

Potential Limitations and Feasibility

1. Data Quality and Availability: The quality and availability of data may vary, which can impact the results. To address this, we will use data cleaning and normalization techniques to ensure consistency. Additionally, collaborations with pharmaceutical companies can provide access to high-quality proprietary data.

2. Computational Resources: The research requires significant computational resources, which may be a constraint. We will leverage cloud-based HPC platforms to mitigate this issue and ensure scalability.

3. Time Constraints: Given the complexity of the research, time management is crucial. We will break down the research into manageable phases with clear milestones to ensure timely completion.

4. Ethical Considerations: Handling sensitive data, such as clinical trial data, requires strict adherence to ethical guidelines. We will ensure data privacy and security by following best practices and obtaining necessary approvals.

By addressing these potential limitations and leveraging the strengths of the chosen methodology, we desire to conduct a thorough and impactful study that advances the field of drug discovery.

Work plan

Research start: 2024/7/04

Week 1-7: Literature review and proposal writing
Week 8-11: Data collection and preprocessing
Week 12-21: Model development and initial training.
Week 22-28: Model validation, scalability testing, and interpretability enhancements.
Week 29-32: Integration of big data analytics.
Week 33-39: Final evaluation and refinement.
Week 40-44: Report writing and presentation
Week 45-49: Publication of research.

Reference