In this paper, we introduce DAUS, a generative The field of dialogue systems has seen a notable user simulator for TOD systems. As depicted in surge in the utilization of user simulation approaches, Figure 1, once initialized with the user goal description, primarily for the evaluation and enhancement DAUS engages with the system across of conversational search systems (Owoicho multiple turns, providing information to fulfill the et al., 2023) and task-oriented dialogue (TOD) systems user's objectives. Our aim is to minimize the commonly (Terragni et al., 2023). User simulation plays observed user simulator hallucinations and a pivotal role in replicating the nuanced interactions incorrect responses (right-hand side of Figure 1), of real users with these systems, enabling a with an ultimate objective of enabling detection wide range of applications such as synthetic data of common errors in TOD systems (left-hand side augmentation, error detection, and evaluation (Wan of Figure 1). Our approach is straightforward yet et al., 2022; Sekulić et al., 2022; Li et al., 2022; effective: we build upon the foundation of LLMbased Balog and Zhai, 2023; Ji et al., 2022).

This paper presents a novel application of large language models in user simulation for task-oriented dialog systems, specifically focusing on an in-context learning approach. By harnessing the power of these models, the proposed approach generates diverse utterances based on user goals and limited dialog examples. Unlike traditional simulators, this method eliminates the need for labor-intensive rule definition or extensive annotated data, making it more efficient and accessible. Additionally, an error analysis of the interaction between the user simulator and dialog system uncovers common mistakes, providing valuable insights into areas that require improvement. Our implementation is available at https://github.com/telepathylabsai/prompt-based-user-simulator.

With the fast development of COVID-19 into a global pandemic, scientists around the globe are desperately searching for effective antiviral therapeutic agents. Bridging systems biology and drug discovery, we propose a deep learning framework for conditional de novo design of antiviral candidate drugs tailored against given protein targets. First, we train a multimodal ligand--protein binding affinity model on predicting affinities of antiviral compounds to target proteins and couple this model with pharmacological toxicity predictors. Exploiting this multi-objective as a reward function of a conditional molecular generator (consisting of two VAEs), we showcase a framework that navigates the chemical space toward regions with more antiviral molecules. Specifically, we explore a challenging setting of generating ligands against unseen protein targets by performing a leave-one-out-cross-validation on 41 SARS-CoV-2-related target proteins. Using deep RL, it is demonstrated that in 35 out of 41 cases, the generation is biased towards sampling more binding ligands, with an average increase of 83% comparing to an unbiased VAE. We present a case-study on a potential Envelope-protein inhibitor and perform a synthetic accessibility assessment of the best generated molecules is performed that resembles a viable roadmap towards a rapid in-vitro evaluation of potential SARS-CoV-2 inhibitors.