KDD AIDSH 2024 aims to foster discussions and developments that push the boundaries of Artificial Intelligence (AI) and Data Science (DS) in healthcare, enhance diagnostic accuracy and promote human-centric approaches to healthcare, thus stimulating future interdisciplinary collaborations. This year's symposium will focus on expanding the application of AI/DS in healthcare/medicine and bridging existing gaps. The workshop invites submissions of full papers as well as work-in-progress on the application of AI/DS in healthcare. The workshop will feature three invited talks from eminent speakers, spanning academia, industry, and clinical researchers. In addition, selected papers will be invited to publish in Health Data Science, a Science Partner Journal. This summary provides a brief description of the half-day workshop to be held on August 26th, 2024. The webpage for the workshop can be found at https://aimel.ai/kdd2024aidsh.

Protein engineering has important applications in drug discovery. Among others, inverse protein folding is a fundamental task in protein design, which aims at generating protein's amino acid sequence given a 3D graph structure. However, most existing methods for inverse protein folding are based on sequential generative models and therefore limited in uncertainty quantification and exploration ability to the entire protein space. To address the issues, we propose a sampling method for inverse protein folding (SIPF). Specifically, we formulate inverse protein folding as a sampling problem and design two pretrained neural networks as Markov Chain Monte Carlo (MCMC) proposal distribution. To ensure sampling efficiency, we further design (i) an adaptive sampling scheme to select variables for sampling and (ii) an approximate target distribution as a surrogate of the unavailable target distribution. Empirical studies have been conducted to validate the effectiveness of SIPF, achieving 7.4% relative improvement on recovery rate and 6.4% relative reduction in perplexity compared to the best baseline.

In recent years, therapeutic antibodies have become one of the fastest-growing classes of drugs and have been approved for the treatment of a wide range of indications, from cancer to autoimmune diseases. Complementarity-determining regions (CDRs) are part of the variable chains in antibodies and determine specific antibody-antigen binding. Some explorations use in silicon methods to design antibody CDR loops. However, the existing methods faced the challenges of maintaining the specific geometry shape of the CDR loops. This paper proposes a Constrained Energy Model (CEM) to address this issue. Specifically, we design a constrained manifold to characterize the geometry constraints of the CDR loops. Then we design the energy model in the constrained manifold and only depict the energy landscape of the manifold instead of the whole space in the vanilla energy model. The geometry shape of the generated CDR loops is automatically preserved. Theoretical analysis shows that learning on the constrained manifold requires less sample complexity than the unconstrained method. CEM's superiority is validated via thorough empirical studies, achieving consistent and significant improvement with up to 33.4% relative reduction in terms of 3D geometry error (Root Mean Square Deviation, RMSD) and 8.4% relative reduction in terms of amino acid sequence metric (perplexity) compared to the best baseline method. The code is publicly available at https://github.com/futianfan/energy_model4antibody_design

The goal of molecular optimization is to generate molecules similar to a target molecule but with better chemical properties. Deep generative models have shown great success in molecule optimization. However, due to the iterative local generation process of deep generative models, the resulting molecules can significantly deviate from the input in molecular similarity and size, leading to poor chemical properties. The key issue here is that the existing deep generative models restrict their attention on substructure-level generation without considering the entire molecule as a whole. To address this challenge, we propose Molecule-Level Reward functions (MOLER) to encourage (1) the input and the generated molecule to be similar, and to ensure (2) the generated molecule has a similar size to the input. The proposed method can be combined with various deep generative models. Policy gradient technique is introduced to optimize reward-based objectives with small computational overhead. Empirical studies show that MOLER achieves up to 20.2% relative improvement in success rate over the best baseline method on several properties, including QED, DRD2 and LogP.

Drug discovery aims at finding promising drug molecules for treating target diseases. Existing computational drug discovery methods mainly depend on molecule databases, ignoring valuable data collected from clinical trials. In this work, we propose PRIME to leverage high-quality drug molecules and drug-disease relations in historical clinical trials to narrow down the molecular search space in drug discovery. PRIME also introduces time dependency constraints to model evolving drug-disease relations using a probabilistic deep learning model that can quantify model uncertainty. We evaluated PRIME against leading models on both de novo design and drug repurposing tasks. Results show that compared with the best baselines, PRIME achieves 25.9% relative improvement (i.e., reduction) in average hit-ranking on drug repurposing and 47.6% relative improvement in success rate on de novo design.