Computational Medicinal Chemistry Dissertation Titles
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Computational Medicinal Chemistry Dissertation Titles

Info: 1557 words(1 pages) Computational Medicinal Chemistry Dissertation Titles
Published: 11th December 2025 in Computational Medicinal Chemistry Dissertation Titles

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Introduction

Computational medicinal chemistry has become an indispensable part of the drug discovery process would be an understatement. Such a method gives a scientist the possibility to predict how a molecule will behave, how the drug’s qualities can be altered in order to make it better, and to simulate its interaction with the biological system so that laboratory testing can be done under optimal conditions. As a result of the joint efforts of quantum chemistry, molecular simulations, and AI, drug discovery has taken a quantum leap in terms of speed, precision, and cost-effectiveness. Nevertheless, these advancements are not without their drawbacks. Firstly, the majority of computational predictions are still in need of laboratory confirmation, acquiring single-crystal structural data is still very challenging, and the number of standard frameworks that connect computational outputs to empirical evidence is still very small. Moreover, the integration of multi-omics data, model transparency, and regulatory-ready workflows is still the major hurdle in the area of drug discovery. The ensuing dissertation titles propose solutions for these problems through the utilisation of explainable AI, high-fidelity computational modelling, structural validation alternatives, and machine learning frameworks that facilitate the link between computational predictions and real-world medicinal chemistry applications.

Computational Medicinal Chemistry Dissertation Titles

1: Integrating Artificial Intelligence into Drug Discovery: Addressing Data, Regulatory, and Ethical Challenges for Reliable PK/PD Predictions

Artificial Intelligence (AI) is a common tool in drug discovery and research, especially in areas of predicting pharmacokinetics (PK) and pharmacodynamics (PD). It has the potential to shorten the whole process of computational drug discovery by faster decision-making, reducing expenses, and improving drug safety profiling. But still, the use of AI in the pharmaceutical industry is limited mainly due to technical, regulatory, and ethical issues as pointed out by Mandle and Rathor (2025) in the Journal of Pharmaceutical Research and Integrated Medical Sciences (JPRIMS). An important challenge among these is the lack of access to standardised and quality datasets, the non-existence of regulatory validation frameworks, and the limited knowledge of the AI algorithms. These issues are the main reason why even the most reliable AI decisions are slow to be transferred from research labs to clinical practices.

Problem Statement:
The lack of common datasets, procedures for validation aligned with regulations, transparent AI models, and interpretable human-AI interfaces leads to the situation that AI-driven PK/PD predictions cannot be used reliably in drug discovery and clinical decision-making.

Research Gap:
During the time of the revolution, the currently existing research, even though showing some positive signs, lacked the quality of providing standardised datasets and parallel validation procedures for AI algorithms in the field of PK/PD prediction. Meanwhile, ethical issues like biases in the algorithm and limited interpretability are still being discussed, while human-AI collaboration is still at the very beginning, mostly due to low model interpretability. The existing limitations entail the non-reproducibility, non-acceptance by the regulators, and non-clinical application of AI drugs.

Research question:
How can the drug discovery process through the integration of AI, especially in PK/PD prediction, be optimised in a manner that ensures reproducibility, regulatory compliance, ethical transparency, and effective clinical translation through interpretability?

Outcome:
The plan for the research is mainly based on a novel framework that will assist AI to facilitate drug discovery processes, particularly by means of PK/PD prediction, through data standardisation, validation to meet regulatory requirements, and the utilisation of Explainable AI (XAI) for assurance of transparency and interpretability. The project will, thus, elevate the whole process of machine learning in pharmacology in terms of trustworthiness, reproducibility, and applicability, thus linking research and patient care more closely.

Reference:

Mandle, N., & Rathor, S. (2025). AI-Enabled Devices in Drug Discovery: Bridging the Gap Between Research and Clinical Application. Journal of Pharmaceutical Research and Integrated Medical Sciences (JPRIMS), 2(2), 25–42.

2. Bridging the Gap Between AI and Clinical Application in Drug Discovery: A Framework for Reliable and Explainable PK/PD Predictions

The incorporation of explainable Artificial Intelligence (AI) in the drug discovery process is remarkable, as it can significantly enhance the accuracy of predictions for pharmacokinetics (PK), pharmacodynamics (PD), and toxicity, thereby accelerating the process of advancing drug candidates into clinical trials. Mandle and Rathor (2025) reported in JPRIMS about how AI-enhanced technologies not only provide support for early decision-making but also help reduce the costs of development. Thus, the full realisation of AI-assisted predictions in clinical practice is a tedious process and still hampered by data quality, model interpretability, regulatory validation, and ethical transparency issues. These are the reasons that hinder the dependable application of AI in PK/PD modelling and limit its embracing by the drug development process.

Problem Statement:
The non-availability of clear, transparent, and regulatory-compliant frameworks for AI-based PK/PD prediction not only weakens the trust and acceptance of AI outputs but also restricts their application in guiding clinical decision-making and drug development.

Research gap:

The current AI-based PK/PD prediction applications are limited by data discrepancies, insufficient guidelines from regulatory bodies, a lack of interpretability, and continuous concerns about ethics, like bias in algorithms and non-transparency, which are some of the main issues. These barriers lead to a situation in which no dependable, reproducible, and clinically usable AI-based predictions can be produced, thus hindering the process of clinical translational drug discovery significantly.

Research Question:
What are the ways to enhance AI-driven PK/PD modelling through data practices that are standardised and aligned with regulations, ethical validation, and interpretability that is improved, to provide reliable drug development decision-making?

Outcome:
An all-encompassing framework will be produced by this study that will unify data practices, add regulatory-compliant validation procedures, and apply Explainable AI (XAI) to enhance transparency, and thus, the support for human-AI collaboration will be even more effective. Consequently, the new framework will greatly increase the trustworthiness, repeatability, and readiness for clinical use of AI-powered PK/PD predictions; hence, their acceptance and use in drug discovery and development will be accelerated.

Reference:

Mandle, N., & Rathor, S. (2025). AI-Enabled Devices in Drug Discovery: Bridging the Gap Between Research and Clinical Application. Journal of Pharmaceutical Research and Integrated Medical Sciences (JPRIMS), 2(2), 25–42.

3. Enhancing Predictive Accuracy in Bioinformatics-Driven Drug Discovery: Integrating Multi-Omics Data, Model Transparency, and High-Fidelity Computational Workflows

Bioinformatics in drug discovery is the most important aspect in the whole process of drug design today, as through it it is possible to predict the interactions between drugs and biological targets, to investigate the pathways of diseases, and to combine different types of biological data. Nevertheless, in a paper by Oraibi et al., published in the Journal of the Colombian Association of Pharmaceutical Sciences (2025), the authors have indicated that the current computational environment is indeed one with many important limitations. It is impossible to model biological systems with their many complexities accurately; furthermore, while multi-omics data integration is still a major challenge for effective integration, and prediction workflows are suffering from the big problem of having to deal with false positives that result from low data quality. These difficulties put together limit the degree to which bioinformatics can guarantee and facilitate the process of drug discovery.

Problem Statement:
The lack of an integrated high-fidelity computational framework that would merge quality-controlled data, multi-omics fusion, and transparent modelling approaches together hampers the ability of bioinformatics tools to produce reliable predictions for drug design and discovery.

Research Gap:

The integration methods of multi-omics that are robust are not yet available, data-quality optimisation has not been given sufficient attention, and no transparent computational models that are capable of accurately representing complex biological interactions have been developed. These limitations will hamper predictive accuracy and reduce the reliability of computational drug-design pipelines.

Research Question:
How to incorporate multi-omics integration, rigorous data-quality workflows, and transparent computational modelling to obtain better predictive accuracy and biological reliability in bioinformatics-assisted drug discovery?

Outcome:
This study will present a fully integrated system comprising quality-oriented data streams, multi-omics merger techniques, and easy-to-understand modelling approaches will be created. This system will not only improve prediction accuracy but will also lower the rate of false positives and increase the credibility of the scientific basis of computerised drug-design methods.

Reference:

Oraibi, A. I., Zheoat, A. M., Sameer, H. N., Alboreadi, M. A., & Abdulhamza, H. M. (2025). Drug design and discovery with bioinformatics tools. Revista Colombiana de Ciencias Químico-Farmacéuticas, 54(3), 621.

4. Advancing Explainable Deep Learning Frameworks for Bioinformatics-Based Drug Discovery: Addressing Data Scarcity, Model Complexity, and Biological Interpretability

Deep learning (DL) technologies have the potential to completely change the way new drugs are discovered by their capacity to analyse large biological data and to recognise complex patterns, predict novel drugs, and even perform large-scale biological analysis. In particular, DL approaches for drug-target interaction prediction can accelerate the identification of potential therapeutic compounds, reducing the time and cost associated with traditional drug development. Still, according to a paper published by Oraibi et al, DL technologies face major drawbacks in terms of their inability to provide interpretable results, lack of data in specialised biomedical areas, and the difficulty in simulating complex biological relationships. The majority of the DL models behave like “black boxes” where the internal workings are not transparent, and thus the researchers do not have an easy time knowing how the predictions were arrived at. This situation lowers the confidence of the researchers, makes it difficult to validate the results and, ultimately, creates hindrances in the acceptance of the new technologies by the scientific and clinical communities.

Problem Statement:
Lack of frameworks for deep learning that are both explainable and data-efficient, which rely on the inherent properties of the compounds being studied, makes the predictions resulting from computational methods in drug discovery less reliable, less biologically relevant, and therefore, limited in their practical use and acceptance among researchers.

Research gap:

Present-day deep learning models used in bioinformatics are not transparent, are unable to deal with small datasets properly, and cannot represent biological complexity with a high level of precision. These drawbacks make the application of such models in drug design less effective and slow down the process of discovering new therapeutic candidates.

Research Question:
Which deep learning for drug discovery techniques that are explainable, data-efficient, and informed by biology can effectively enhance interpretability, predictive accuracy, and practical utility in bioinformatics-based drug design and discovery?

Outcome:
The study will provide an extensive AI framework focused on the development of new drugs. It will have versatile and interpretable neural designs, data-efficient learning methods that include few-shot and transfer learning, and pipelines that validate the biological aspect of the research. All these innovations will come together to improve the transparency, the support of predictive performance and the use of DL in an ethical way during computer-aided drug development.

Reference:

Oraibi, A. I., Zheoat, A. M., Sameer, H. N., Alboreadi, M. A., & Abdulhamza, H. M. (2025). Drug design and discovery with bioinformatics tools. Revista Colombiana de Ciencias Químico Farmacéuticas, 54(3), 621.

5. Advancing Machine Learning for Computer-Aided Drug Design: Developing Generalizable, Interpretable, and Biologically Grounded Predictive Frameworks

The application of machine learning (ML) has greatly influenced the field of computer-aided drug design (CADD) by providing quick forecasting of the interactions of the compounds, their biological activities, and the properties necessary for marketing. Nevertheless, Perišić, Bayrak, and Gunady (2025) point out issues in the use of ML models in drug development in their article in Frontiers in Molecular Biosciences. Some of the shortcomings are limited to the training data, very poor generalisation, weaker interpretability, which is because these models work as “black-boxes”, and the use of easy-to-handle ML libraries that come with superficial analyses leading to neglect of the biological complexity as the main cause of those risks. All these deficiencies stand in the way of the ML tools being able to accurately and reliably make high-stakes decisions in drug development that are high-stakes.

Problem Statement:
The lack of generalizable, interpretable, and biologically contextualised ML frameworks keeps cheminformatics machine learning

dependent on unreliable predictions and insufficient understanding of the molecular phenomena being modelled.

Research gap:

The limitations of the present ML model’s generalisation outside the distribution, very poor mechanistic interpretability and the frequent deployment without the integration of biological knowledge underneath are the main issues that hinder CADD predictions made through ML from being considered reliable and biologically meaningful.

Research Question:
What modifications can be made to machine learning models so that they become better at generalising outside the training space, are easy to interpret and connect up with biological knowledge to produce predictions of remarkable reliability and significance for computer-aided drug design?

Outcome:
The intended research will be a ground-breaking ML framework for CADD that will support the methods of generalisation improvement, integration of explaionable AI (XAI) modules, and biologically informed modelling approaches. This framework will not only be able to deliver accurate molecular structure predictions but will also enhance biological interpretability and thus make the drug design process more reliable for the application of ML through its reliability.

Reference:

Perišić, O., Sevim Bayrak, C., & Gunady, M. K. (2025). Machine learning in computer-aided drug design. Frontiers in molecular biosciences, 12, 1568437.

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