This paper presents a novel Chemist-AI Alignment framework for generating novel structures of quaternary ammonium compounds (QACs), a crucial class of antimicrobial agents.The framework uniquely integrates AI-driven small molecule generation with iterative feedback from chemist experts, leveraging both rapid assessments and comprehensive wet-lab validations to optimize for biological potency and synthetic feasibility.Central to the framework is a hierarchical generative model that captures the QAC hierarchical topology.Extensive experiments highlight the efficacy of the framework in identifying promising QAC candidates, many

Designing molecules with desired biological properties remains an outstanding challenge both in the wet and dry laboratories. Meeting this challenge promises great translational impacts across drug discovery, material sciences, biotechnology, and more. Recent momentum in deep learning promises to advance our computational capabilities on molecule generation. In particular, deep graph generative models which treat molecule design as a graph generation problem are allowing us to directly learn from existing databases of small molecules and generate novel, valid molecules. Currently, these models have many shortcomings, including poor controllability of desired molecular properties, especially in practical application where the training data is usually small, noisy, and incomplete. This paper focuses on equipping graph variational autoencoders with the ability to control for desired properties and its practical application in a practical application which is the generation of Quaternary Ammonium Compounds (QAC). Several controllable graph generation mechanisms are investigated for their effectiveness. A general framework is then proposed to extend these mechanisms by our newly proposed objective function to handle the challenges in practical applications where the property value annotations are usually censored and not fully available in all training samples. The experimental evaluation considers an experimentally-characterized dataset of antimicrobial small molecules with wet-lab characterized activity against antibiotic-resistant bacteria. Extensive experiments demonstrate the superiority of the proposed models and control of desired properties.

Activity characterization, optimization, and generation of small molecules are increasingly active areas of research at the intersection of molecular chemistry and machine learning. Large datasets of small molecules have allowed training deep models that have been shown capable of exploring the underlying chemical space and generating valid, novel, and unique molecules. While this is a noteworthy achievement, what impedes operationalizing these models in the wet laboratory is the ability to link the chemical and biological space of small molecules. A central challenge to this is the lack of activity data on these entities. In this paper we relate a computational pipeline that permits linking the chemical and biological space of an important class of small molecules, quaternary ammonium compounds (QACs). Our experimental collaborators have characterized the activity of many QACs against Staphylococcus aureus. We train various generative models and evaluate their ability to generate valid, novel, and unique QACs. We then leverage classification models trained over activity data to evaluate the generated QACs. The resulting pipeline identifies valid, novel, unique, membrane-active QACs. This work opens the way to further avenues of research in machine learning models capable of jointly sampling the chemical and biological space of small molecules.

Developing deep generative models has been an emerging field due to the ability to model and generate complex data for various purposes, such as image synthesis and molecular design. However, the advance of deep generative models is limited by the challenges to generate objects that possess multiple desired properties because: 1) the existence of complex correlation among real-world properties is common but hard to identify; 2) controlling individual property enforces an implicit partially control of its correlated properties, which is difficult to model; 3) controlling multiple properties under variour manners simultaneously is hard and underexplored. We address these challenges by proposing a novel deep generative framework that recovers semantics and correlation of properties through disentangled latent vectors. The correlation is handled via an explainable mask pooling layer, and properties are precisely retained by the generated objects via the mutual dependence between latent vectors and properties. Our generative model preserves properties of interest while handles correlation and conflicts of properties under a multi-objective optimization framework. The experiments demonstrate our model's superior performance in generating objects with desired properties.