GeoCycler aligns latent diffusion models via reward-weighted training with a type-gated stair reward to raise cyclic peptide closure rates across multiple topologies on the LNR benchmark.
arXiv preprint arXiv:2209.15408 , year=
4 Pith papers cite this work. Polarity classification is still indexing.
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APCyc is a target-aware generative model for de novo cyclic peptide design that adds cyclization-site encoding and Bayesian guidance to jointly optimize physicochemical properties.
ParetoPilot uses Infer-Perturb-Guide inside reverse diffusion to push samples to the Pareto front without surrogates, ranking best among 16 methods on 51 offline MOO tasks.
CrysLDNet combines VAE and latent diffusion pretraining on unlabeled crystals to improve graph encoder performance on property prediction by about 4-5% on JARVIS and MP datasets.
citing papers explorer
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GeoCycler: Reward-Aligned 3D Diffusion for Constraint-Conditioned Cyclic Peptide Design
GeoCycler aligns latent diffusion models via reward-weighted training with a type-gated stair reward to raise cyclic peptide closure rates across multiple topologies on the LNR benchmark.
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APCyc: Property-Informed Design of Cyclic Peptides via Automated Cyclization
APCyc is a target-aware generative model for de novo cyclic peptide design that adds cyclization-site encoding and Bayesian guidance to jointly optimize physicochemical properties.
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ParetoPilot: Zero-Surrogate Offline Multi-Objective Optimization via Infer-Perturb-Guide Diffusion
ParetoPilot uses Infer-Perturb-Guide inside reverse diffusion to push samples to the Pareto front without surrogates, ranking best among 16 methods on 51 offline MOO tasks.
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Latent Diffusion Pretraining for Crystal Property Prediction
CrysLDNet combines VAE and latent diffusion pretraining on unlabeled crystals to improve graph encoder performance on property prediction by about 4-5% on JARVIS and MP datasets.