REVIEW 8 cited by
Equivariant Energy-Guided SDE for Inverse Molecular Design
Not yet reviewed by Pith; the record is open.
This paper has not been read by Pith yet. Machine review is queued; the pith claim, tier, and objections will appear here once it completes.
SPECIMEN: schema-true, not a live event
T0 review · schema-true
One-sentence machine reading of the paper's core claim.
pith:XXXXXXXX · record.json · timestamp
read the original abstract
Inverse molecular design is critical in material science and drug discovery, where the generated molecules should satisfy certain desirable properties. In this paper, we propose equivariant energy-guided stochastic differential equations (EEGSDE), a flexible framework for controllable 3D molecule generation under the guidance of an energy function in diffusion models. Formally, we show that EEGSDE naturally exploits the geometric symmetry in 3D molecular conformation, as long as the energy function is invariant to orthogonal transformations. Empirically, under the guidance of designed energy functions, EEGSDE significantly improves the baseline on QM9, in inverse molecular design targeted to quantum properties and molecular structures. Furthermore, EEGSDE is able to generate molecules with multiple target properties by combining the corresponding energy functions linearly.
Forward citations
Cited by 8 Pith papers
-
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.
-
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.
-
ParetoPilot: Zero-Surrogate Offline Multi-Objective Optimization via Infer-Perturb-Guide Diffusion
ParetoPilot is a zero-surrogate diffusion framework for offline MOO that uses an IPG engine to steer generation via inferred objective directions and orthogonal perturbations, outperforming 14 baselines on 51 tasks.
-
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.
-
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.
-
Do we need equivariant models for molecule generation?
Rotation-augmented CNNs learn equivariance easily for denoising and prediction, but only large models keep generation outputs invariant to seed rotations, and their latent codes do not identify rotated molecules as th...
-
Zero-Shot Cyclic Peptide Design via Composable Geometric Constraints
CP-Composer trains a geometric diffusion model on linear peptides and imposes cyclization constraints at generation time, achieving 38-84% constraint satisfaction across four cyclization strategies.
-
Controllable 3D Molecular Generation for Structure-Based Drug Design Through Bayesian Flow Networks and Gradient Integration
Gradient guidance inside Bayesian Flow Network updates generates 3D drug candidates with stronger predicted docking scores, better retrosynthesis feasibility, and improved kinase selectivity than diffusion baselines.
Discussion (0). Sign in to comment.