Lead optimization is a pivotal task in the drug design phase within the drug discovery lifecycle. The primary objective is to refine the lead compound to meet specific molecular properties for progression to the subsequent phase of development. In this work, we present an innovative approach, Latent Evolutionary Optimization for Molecule Generation (LEOMol), a generative modeling framework for the efficient generation of op-timized molecules. LEO Mol leverages Evolutionary Algorithms, such as Genetic Algorithm and Differential Evolution, to search the latent space of a Variational AutoEncoder (VAE). This search facilitates the identification of the target molecule distribution within the latent space. Our approach consistently demonstrates superior performance compared to previous state-of-the-art mod-els across a range of constrained molecule generation tasks, outperforming existing models in all four sub-tasks related to property targeting. Additionally, we suggest the importance of including toxicity in the evaluation of generative models. Furthermore, an ablation study underscores the improvements that our approach provides over gradient-based latent space optimization methods. This underscores the effectiveness and superiority of LEO Mol in addressing the inherent challenges in constrained molecule generation while emphasizing its potential to propel advancements in drug discovery.

Large Language Models (LLMs) have demonstrated remarkable capabilities in various language-related tasks enabling applications in various fields such as healthcare, education, financial services etc. However, they are prone to producing factually incorrect responses or ''hallucinations'' which can have detrimental consequences such as loss of credibility, diminished customer trust etc. In this presentation, we showcase a solution that addresses the challenge of minimizing hallucinations. Our solution provides accurate responses and generates detailed explanations, thereby enabling the users to know how the model arrived at the final response. Additionally, it verifies if the explanations are factually correct and offers insights into whether the generated explanations are directly derived from the provided context or if they are inferred from it. We also systematically assess the quality of generated responses using an LLM-based evaluation technique. We present empirical results on benchmark datasets to demonstrate the effectiveness of our approach. Our presentation also examines the impact of individual components in the solution, enhancing the factual correctness of the final response. This research is vital for industries utilizing LLMs, as it provides a means to enhance the reliability of responses and mitigate the risks associated with factual hallucinations. Researchers and practitioners seeking to enhance the reliability of LLM responses will find valuable insights in this presentation.

Pharmacovigilance is the science and practice of monitoring, assessing, and preventing adverse effects or any other drug-related problems. Pharmacovigilance ensures the post-market safety of pharmaceuticals and plays a crucial role in public health by enhancing drug safety. This discipline involves collecting, analyzing, and reporting data on adverse events, allowing for informed regulatory decisions. Manual systems face challenges in handling data volume, potentially leading to oversight and delays. Automation with advanced technologies can be a practical solution to mitigate these challenges and ensure efficient data management.

Foundational Large Language Models (LLMs) are pre-trained generally on huge corpora encompassing broad subjects to become versatile and generalize to future downstream tasks. However, their effectiveness falls short when dealing with tasks that are highly specialized to a specific use case. Even when adopting current prompt engineering techniques like few-shot or Chain-of-Thought reasoning prompts, the required level of results is not yet achievable directly with foundational models alone. The alternative approach is to fine-tune the LLM, but a common challenge is the limited availability of task-specific training data. In this talk, we will introduce an end-to-end automated framework to tailor a model to specific downstream tasks for an industry where the first step is to generate task-specific custom data from unstructured documents. Next, we will discuss our optimized distributed training pipeline for fine-tuning LLMs on the generated data. Finally, we will provide an overview of the statistical metrics and customized metrics we employ for assessing the performance of the fine-tuned LLM. This automated framework alleviates the burden of manual adjustments and streamlines the process to provide a model that is fully customized to suit the unique requirements of any specific business use case.