Mol-CycleGAN - a generative model for molecular optimization
Designing a molecule with desired properties is one of the biggest challenges in drug development, as it requires optimization of chemical compound structures with respect to many complex properties. To augment the compound design process we introduce Mol-CycleGAN - a CycleGAN-based model that generates optimized compounds with high structural similarity to the original ones. Namely, given a molecule our model generates a structurally similar one with an optimized value of the considered property. We evaluate the performance of the model on selected optimization objectives related to structural properties (presence of halogen groups, number of aromatic rings) and to a physicochemical property (penalized logP). In the task of optimization of penalized logP of drug-like molecules our model significantly outperforms previous results.
Code (1)
Similar Papers 제목 키워드 기반
Identity-Aware CycleGAN for Face Photo-Sketch Synthesis and Recognition
Face photo-sketch synthesis and recognition has many applications in digital entertainment and law enforcement. Recently, generative adversarial networks (GANs) based methods have significantly improved the quality of im…
Image GenerationSketch RecognitionTripletHierarchical-level rain image generative model based on GAN
Autonomous vehicles are exposed to various weather during operation, which is likely to trigger the performance limitations of the perception system, leading to the safety of the intended functionality (SOTIF) problems. …
Autonomous VehiclesGenerative Adversarial NetworkSSIMPlasma-CycleGAN: Plasma Biomarker-Guided MRI to PET Cross-modality Translation Using Conditional CycleGAN
Cross-modality translation between MRI and PET imaging is challenging due to the distinct mechanisms underlying these modalities. Blood-based biomarkers (BBBMs) are revolutionizing Alzheimer's disease (AD) detection by i…
Image GenerationTranslationxAI-CycleGAN, a Cycle-Consistent Generative Assistive Network
In the domain of unsupervised image-to-image transformation using generative transformative models, CycleGAN has become the architecture of choice. One of the primary downsides of this architecture is its relatively slow…
counterfactualExplainable Artificial Intelligence (XAI)Learning Spatial Pyramid Attentive Pooling in Image Synthesis and Image-to-Image Translation
Image synthesis and image-to-image translation are two important generative learning tasks. Remarkable progress has been made by learning Generative Adversarial Networks (GANs)~\cite{goodfellow2014generative} and cycle-c…
Image GenerationImage-to-Image TranslationTranslation