This paper presents the Model Gateway, a management platform for managing machine learning (ML) and scientific computational models in the drug discovery pipeline. The platform supports Large Language Model (LLM) Agents and Generative AI-based tools to perform ML model management tasks in our Machine Learning operations (MLOps) pipelines, such as the dynamic consensus model, a model that aggregates several scientific computational models, registration and management, retrieving model information, asynchronous submission/execution of models, and receiving results once the model complete executions. The platform includes a Model Owner Control Panel, Platform Admin Tools, and Model Gateway API service for interacting with the platform and tracking model execution. The platform achieves a 0% failure rate when testing scaling beyond 10k simultaneous application clients consume models. The Model Gateway is a fundamental part of our model-driven drug discovery pipeline. It has the potential to significantly accelerate the development of new drugs with the maturity of our MLOps infrastructure and the integration of LLM Agents and Generative AI tools.

Drug discovery is a long, expensive, and complex process, relying heavily on human medicinal chemists, who can spend years searching the vast space of potential therapies. Recent advances in artificial intelligence for chemistry have sought to expedite individual drug discovery tasks; however, there remains a critical need for an intelligent agent that can navigate the drug discovery process. Towards this end, we introduce LIDDiA, an autonomous agent capable of intelligently navigating the drug discovery process in silico. By leveraging the reasoning capabilities of large language models, LIDDiA serves as a low-cost and highly-adaptable tool for autonomous drug discovery. We comprehensively examine LIDDiA, demonstrating that (1) it can generate molecules meeting key pharmaceutical criteria on over 70% of 30 clinically relevant targets, (2) it intelligently balances exploration and exploitation in the chemical space, and (3) it can identify promising novel drug candidates on EGFR, a critical target for cancers.

Traditional drug discovery is a long, expensive, and complex process. Advances in Artificial Intelligence (AI) and Machine Learning (ML) are beginning to change this narrative. Here, we provide a comprehensive overview of different AI and ML tools that can be used to streamline and accelerate the drug discovery process. By using data sets to train ML algorithms, it is possible to discover drugs or drug-like compounds relatively quickly, and efficiently. Additionally, we address limitations in AI-based drug discovery and development, including the scarcity of high-quality data to train AI models and ethical considerations. The growing impact of AI on the pharmaceutical industry is also highlighted. Finally, we discuss how AI and ML can expedite the discovery of new antibiotics to combat the problem of worldwide antimicrobial resistance (AMR).

If there's one thing we can all agree upon, it's that the 21st century's captains of industry are trying to shoehorn AI into every corner of our world. But for all of the ways in which AI will be shoved into our faces and not prove very successful, it might actually have at least one useful purpose. Risk mitigation isn't a sexy notion but it's worth understanding how common it is for a new drug project to fail. To set the scene, consider that each drug project takes between three and five years to form a hypothesis strong enough to start tests in a laboratory. A 2022 study from Professor Duxin Sun found that 90 percent of clinical drug development fails, with each project costing more than 2 billion.

We investigate the following data mining problem from Computational Chemistry: From a large data set of compounds, find those that bind to a target molecule in as few iterations of biological testing as possible. In each iteration a comparatively small batch of compounds is screened for binding to the target. We apply active learning techniques for selecting the successive batches. One selection strategy picks unlabeled examples closest to the maximum margin hyperplane. Another produces many weight vectors by running perceptrons over multiple permutations of the data.

Structure-based drug discovery (SBDD) is a standard method for identifying prospective medications for a target by leveraging its structural information. AlphaFold, a technique for predicting protein structure, has been regarded as a helpful resource for the discovery of therapeutics for new targets with low or no structural knowledge. In this study, the scientists utilized AlphaFold predictions as input for their AI-powered drug discovery engines (PandaOmics and Chemistry42) to efficiently identify a potential drug for CDK20 within 30 days. Understanding the structure of proteins is important in order to figure out their functions and the effect of change in amino acid sequence. The 3D structure of a protein allows us to visualize its functions and how genes and diseases are connected.

Artificial intelligence (AI) has the potential to revolutionize the drug discovery process, offering improved efficiency, accuracy, and speed. However, the successful application of AI is dependent on the availability of high-quality data, the addressing of ethical concerns, and the recognition of the limitations of AI-based approaches. In this article, the benefits, challenges and drawbacks of AI in this field are reviewed, and possible strategies and approaches for overcoming the present obstacles are proposed. The use of data augmentation, explainable AI, and the integration of AI with traditional experimental methods, as well as the potential advantages of AI in pharmaceutical research are also discussed. Overall, this review highlights the potential of AI in drug discovery and provides insights into the challenges and opportunities for realizing its potential in this field. Note from the human-authors: This article was created to test the ability of ChatGPT, a chatbot based on the GPT-3.5 language model, to assist human authors in writing review articles. The text generated by the AI following our instructions (see Supporting Information) was used as a starting point, and its ability to automatically generate content was evaluated. After conducting a thorough review, human authors practically rewrote the manuscript, striving to maintain a balance between the original proposal and scientific criteria. The advantages and limitations of using AI for this purpose are discussed in the last section.

Dr. Tim Guilliams, co-founder and CEO of Healx, is an advocate for harnessing the power of AI to accelerate treatments for rare diseases. Rare diseases are largely overlooked by pharmaceutical companies and traditional drug discovery methods, despite the fact that 1 in 17 people in the UK will be affected by one at some point in their life. Sadly, most rare conditions don't have an approved therapy today, and, in the UK, that means that many of the millions of people dealing with a rare disease will not have access to the treatment--or receive the level of care--that they need. So any step taken by governments to improve outcomes for those with rare diseases is to be welcomed, which is why it was positive to see the Department of Health and Social Care publish its first England Rare Diseases Action Plan this spring, building on the wider UK Rare Diseases Framework published last year. It contains some good proposals on improving diagnosis and access to specialist care--which are both vital--but, arguably, something that will have one of the biggest impacts is unlocking new treatments and making them available to patients. Care is key, but so is an investment in finding cures.

The goal of drug discovery and development is to bring new medicines to patients suffering from critical illnesses. Earlier, drug discovery was a tedious process. Bringing a drug to market still takes 10 to 15 years. As a result, there is a lot of interest in finding new approaches to developing drugs using novel technological approaches. Machine learning tools and techniques are proving their importance at every stage of drug discovery and reducing the risk, and lowering the cost and expenditure used in clinical trials. It proves crucial in QSAR analysis, de novo drug design, hit discoveries, target validation, prognostic biomarkers, digital pathology, etc. The discovery of a drug has a lengthy procedure to go through before reaching the market.

MIT scientists have developed a machine learning model that proposes new molecules for the drug discovery process, while ensuring the molecules it suggests can actually be synthesized in a laboratory. A new artificial intelligence technique has been developed that only proposes candidate molecules that can actually be produced in a lab. Pharmaceutical companies are using artificial intelligence to streamline the process of discovering new medicines. Machine-learning models can propose new molecules that have specific properties which could fight certain diseases, accomplishing in minutes what might take humans months to achieve manually. But there's a major hurdle that holds these systems back: The models frequently suggest new molecular structures that are difficult or impossible to produce in a laboratory.