Drug discovery and delivery are fundamental aspects of the pharmaceutical industry. Discovering a new drug involves significant financial investments, time and a high rate of failure. The Drug discovery involves identifying potential drug candidate form over millions of drugs, analyzing the molecular properties, clinical effects, toxicity etc. Post drug discovery, innovating and finalizing the drug delivery systems require ensuring the precise administration of drugs to have maximum bioavailability with minimum side effects. Challenges like poor bioavailability, drug resistance, and complex patient needs complicates the process.  Over the years diseases have become more complex and personalized medicine is gaining prominence, thus the need for innovative solutions in drug discovery and delivery is more than ever.

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The advent of artificial intelligence (AI) is proving to be a game changer, transforming process of drug discovery to drug delivery. Identifying potential molecules, precise target discovery, toxicity prediction and clinical trial optimization are the key challenging phases of drug discovery in the current times. Innovative AI models enable analysis of large dataset and identification of specific patterns to get the best potential molecules. The AI models are reducing the huge time and cost required to discover a target molecule along with reduced failures.

The latest innovations in artificial intelligence, have the potential to identify eligible patients for clinical trials form a large patient dataset, making patient recruitment efficient. In new drug discovery, more that 30% of potential new drug candidates fail due to toxicity problems. The AI algorithms can also predict abnormalities and predicted outcomes of trial based on historical data of the patient, thereby reducing the chances of failure of the trial.

Smart drug delivery systems, targeted delivery, predictive modelling and personalised medicine are the need of time. To effectively manage these aspects for a new molecule, companies have always faced challenge. The machine learning AI algorithms are facilitating the development of smart drug delivery systems that are designed according to patient needs. AI Integrated smart drug delivery systems can also monitor physiological factors like blood glucose, heart rate etc in addition to drug delivery.  Combination of artificial intelligence with nanotechnology is enabling the development of drug carriers that can directly deliver the drugs to the exact target cell. This is resulting in increased efficacy with lower side effects for the drug. 

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The study of drug pharmacokinetics & pharmacodynamics to develop a successful drug delivery system. Leveraging AI the companies can optimize their drug delivery by studying the ADME (Absorption, distribution, metabolism, and excretion) in detail, resulting in achieving maximum drug bioavailability with minimum toxic effect. In addition, the latest developments in artificial intelligence are opening the path to address the current challenges in the development of personalise medicine. AI enabled drug delivery systems will have to potential to analyse the patient’s body identifying patient specific dose requirements to achieve maximum efficacy.

The pharma industry is leveraging AI power for reshaping discovery and deliver of drugs. However still there are few hurdles limiting the full potential use of AI. The inconsistent and fragmented data with privacy issues limit data accessibility for training of AI models for drug delivery which a key concern. The complexity, cost and regulatory hurdles limit the AI implementation further.

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The global healthcare data accessibility will be transformed through international collaborations with AI platforms.  Pharmaceuticals, AI innovators and regulators collaboration will play a key role in in addressing many of the current challenges with AI implementation. In the near future, the AI models are expected to be highly sophisticated facilitating groundbreaking breakthroughs in reducing drug development timelines and precision medicine. 

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