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AI-Based Drug Target Interactions and Computational Drug Discovery DissertationTitles | phdassistance.com

Info: AI-Based Drug Target Interactions and Computational Drug Discovery DissertationTitles | phdassistance.com

Published: 01th June 2026 in AI-Based Drug Target Interactions and Computational Drug Discovery DissertationTitles | phdassistance.com

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Introduction

Rapid advancement in the field of artificial intelligence and computer technology has resulted in tremendous changes to the pharmaceutical sector by increasing efficiency during drug discovery processes. Traditional approaches to discovering new drugs take a long time and are costly, with high failure rates. The use of AI-Based Drug Target Interactions can serve as an efficient method to discover relations between various therapeutic molecules and targets. Machine learning, deep learning, bioinformatics, and molecular modelling are some of the advanced tools that help to analyse large datasets of biological and chemical information with higher precision. They help with target identification, virtual screening, lead optimisation, and drug repurposing.

Proposed PhD Title 1: AI-Driven Drug–Target Interaction Prediction Frameworks for Accelerating Computational Drug Discovery

Drug-target interactions (DTIs) are an integral part of drug discovery since they help to determine the ability to interact with specific targets and exert their intended therapeutic effects. However, conventional approaches used for detecting drug-target interactions typically involve costly and time-consuming laboratory experiments that yield low success rates. Advances in the field have been accompanied by the increased availability of biological, chemical, and pharmacological data, creating favourable conditions for the use of artificial intelligence techniques in computational drug discovery. Machine learning algorithms, deep learning techniques, and network analysis can be applied to massive data and detect interactions that may be difficult to detect using traditional methods. As argued by Yang & Cheng (2025), AI-based DTI prediction approaches may improve prediction accuracy and accelerate drug discovery processes.  

Problem Statement:
Currently, most methods to identify potential targets operate by analysing biological data in isolation. They cannot capture the interactions between multiple targets within metabolic pathways and thus cannot identify targets efficiently and promptly. In addition, the lack of integration in existing methods delays the process of identifying effective targets. Hence, it would be better to have an AI system to identify and validate targets.

Research Gap:
The existing research is predominantly concerned about single AI approaches like machine learning, deep learning, or networks to predict drug-target interactions. There has been little investigation into integrated approaches that incorporate multiple AI models along with different biomedical datasets. Consequently, the issues of prediction precision, scalability, and interpretation continue to pose difficulties.

Research Question:
How does the use of methods based on artificial intelligence contribute to the efficiency of predicting drug-target interactions?

Outcome:
The outcome of this research is expected to be the development of an entire framework consisting of Machine Learning in Drug Discovery, deep learning networks, and network pharmacology.

Reference:

Yang, Y., & Cheng, F. (2025). Artificial intelligence streamlines scientific discovery of drug–target interactions. British Journal of Pharmacology. https://bpspubs.onlinelibrary.wiley.com/doi/10.1111/bph.17427

AI-Based Drug Target Interactions

Proposed PhD Title 2. Intelligent Multi-Omics Target Identification Systems Using Artificial Intelligence for Precision Drug Discovery

Therapeutic target identification plays a very important role in drug discovery as it helps scientists understand the disease mechanism and devise appropriate treatments for it. Advances made in genomics, proteomics, transcriptomics, and metabolomics have made available much biological data that needs further computational analysis. Artificial intelligence provides an excellent platform for integration and mining of such big data for target identification. AI in Drug Discovery can make significant contributions to the process by recognising patterns and making decisions in the early stage of drug discovery. As stated by Ocana et al. (2025), AI-driven multi-omics will contribute to target identification and enable the development of precision drugs for various diseases.

Problem Statement:
Lead optimisation currently involves many iterations and costly tests in laboratory settings, making the process long and expensive. Current methods may not predict the results accurately based on the physical attributes. Therefore, an AI system to assist with lead optimisation is required.

Research Gap:
In most previous studies, there is either a single-omics approach or AI-based methods to find suitable targets. However, such approaches usually fall short of recognising the intricate correlations among different biological data sets. Little work has been done for employing AI-based systems that can use multi-omics data for target identification.

Research question:

How does multi-omics integration through the use of AI help identify therapeutic targets during precision drug development?

Result:

In this study, research will be conducted to create a multi-omics framework powered by AI that would help in identifying and validating therapeutic targets.

Reference:

Ocana, A., et al. (2025). Integrating Artificial Intelligence in Drug Discovery and Early Drug Development: A Transformative Approach. https://pubmed.ncbi.nlm.nih.gov/40087789/

Proposed PhD Title 3. Deep Learning-Based Molecular Design and Lead Optimisation Models for Next-Generation Drug Development

Lead optimisation and molecular design are key components of drug discovery, crucial for determining efficacy, safety, and pharmacology. Traditional lead optimisation strategies involve a lot of laboratory experiments, which are costly and time-consuming. Due to the development of new Artificial Intelligence for Drug Discovery methods such as deep learning, generative artificial intelligence, and neural networks, molecule design and optimisation have been totally transformed. With the help of Artificial Intelligence for Drug Development technologies, it is now possible to create new molecules and even predict their properties by doing fewer tests. This means that there will be fewer failure cases when developing a drug, which can help to speed up the optimisation process. As claimed by Pathak et al. (2025), using AI technologies allows us to achieve these goals.

Problem Statement:
Traditional lead optimisation techniques are laborious due to expensive and time-consuming experiments, leading to higher expenses and delays in developing the final products. The traditional technique may fail to predict the properties of the molecule and the efficiency of the drugs in advance. Thus, there is a necessity for an AI model that can facilitate the process.

Research Gap:
The current work has mostly been concerned with molecular generation, property predictions, or lead optimisation, being three distinct processes. Not many works have considered developing an intelligent drug discovery framework that would integrate all three tasks—molecular generation, pharmacological properties prediction, and lead optimisation.

Research Question:
How can the virtual screening process using AI systems be improved to detect more candidates for future drugs?

Outcome:
The proposed study will lead to the development of an intelligent virtual screening and molecular docking system that will detect future drug candidates.

Reference:

Pathak, A., et al. (2025). AI-Enabled Drug and Molecular Discovery: Computational Methods, Platforms, and Translational Horizons. https://link.springer.com/article/10.1007/s44345-025-00037-5

Proposed PhD Title 4. AI-Powered Virtual Screening and Molecular Docking Frameworks for High-Throughput Drug Candidate Discovery

Virtual screening and molecular docking have been used frequently to discover drugs from very large databases of chemicals. These approaches have allowed researchers to understand interactions and choose drug molecules. However, the classical approach is resource-intensive and difficult to handle larger datasets. Artificial intelligence has advanced the area of virtual screening through increased accuracy, molecule selection automation, and rapid finding of potential drugs. AI docking has allowed scientists to analyse molecular structures and accurately predict their interactions. In addition, Verma & Kumar (2025) state that artificial intelligence has made virtual screening faster and more efficient in the discovery of potential drugs.

Problem Statement:
The technologies for virtual screening and molecular docking have limitations in handling big data while improving Drug Target Prediction efficiency. Conventional techniques synthesise a high number of candidate molecules that will need expensive verification processes. Hence, AI-based screening platforms are essential to enhance hit detection and boost drug development.

Research Gap:
Existing work highlights that virtual screening and molecular docking have focused more on enhancing each technique individually. Very few works consider combining AI-powered virtual screening, molecular docking, and biological activity prediction for a more integrated discovery process.

Research Question:

In what way is resource management facilitated with automation technology in smart agriculture?

Outcome:
This research work will create a model for intelligent automation farming that promotes agricultural automation and precision farming.

Reference:

Verma, V., & Kumar, D. (2025). Artificial Intelligence and Machine Learning in Drug Discovery: From Lead Discovery to Clinical Validation. https://www.sciencedirect.com/science/article/pii/S1570180826000710

Proposed PhD Title 5. Explainable Artificial Intelligence Models for Drug Repurposing and Personalised Therapeutic Discovery

Repurposing drugs has now become an important approach in drug development since it provides researchers with an opportunity to find new uses for existing drugs, thus saving money on developing new drugs from scratch. The use of artificial intelligence helps researchers analyse biomedical, clinical, and molecular datasets and discover drug-disease connections that may not have been revealed before. Even though there have been successful cases of drug repurposing through AI, there are still many predictive models that operate as black boxes, thus preventing users from understanding the reasoning behind predictions. Explainable Artificial Intelligence, or XAI, can help improve interpretability and build trust in AI applications in the healthcare industry. As noted by Odah (2025), artificial intelligence in drug repurposing can contribute to personalised medicine, but increased interpretability of such models is necessary.

Problem Statement:
The AI repurposing models can be described as black boxes, in that the results produced by the algorithms are not easily interpretable. This limits the trustworthiness of such AI drug repurposing models, limiting their adoption in the development of new drugs. Thus, there is a need for developing explainable AI platforms.

Research Gap:                     
The studies in the field of AI-based drug repurposing concentrate on prediction accuracy and drug-disease interaction. But the lack of explainability and interpretability is still an understudied topic. There are not enough papers that investigate interpretable AI algorithms for the drug repurposing process.

Research Question:
How does the implementation of explainable AI influence the accuracy and transparency of drug repurposing algorithms?

Outcome:
The expected result of the study will be an explanation system using explainable AI that will enable more accurate and transparent results regarding drug repurposing.

Reference:

Odah, M. (2025). Artificial Intelligence Meets Drug Discovery: A Systematic Review on AI-Powered Target Identification and Molecular Design. https://www.preprints.org/manuscript/202503.0912

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