Journal: Journal of Chemical Information and Modeling

Article Title: SILVR: Guided Diffusion for Molecule Generation

doi: 10.1021/acs.jcim.3c00667

Figure Lengend Snippet: Schematic of the equivariant diffusion model with selective iterative latent variable refinement (SILVR) indicated for every denoising step. Here, the reference in blue on the left shows 3 small fragments. They evolve over time t in the diffusion process to resemble a Gaussian distribution at t = T , see eq . The β represents the noise added at each step, and the dots show the steps omitted from time t = 3 to t = T . As atoms effectively “diffuse”, they can be perceived as changing position. To generate a new sample, a sample is generated from p θ ( x ) according to eq , this distribution is from the learned EDM. At each denoising step, a set of reference fragments ( y t ) at that same level of noise t is used, which is indicated by the SILVR arrows to condition the EDM. This is controlled through SILVR at a given rate r S , until a new sample that resembles the reference is generated (following the bottom row along the yellow boxes and EDM arrows).

Article Snippet: The agreement in the shape of the samples and the binding site of MPro were determined using the OpenEye toolkit version 2022.2.2 Gaussian scoring function Shapegauss., This scoring function measures the shape complementarity between the ligand and receptor by considering each heavy atom as a Gaussian function.

Techniques: Diffusion-based Assay, Generated

Journal: Journal of Chemical Information and Modeling

Article Title: SILVR: Guided Diffusion for Molecule Generation

doi: 10.1021/acs.jcim.3c00667

Figure Lengend Snippet: Validation measures of the SILVR model using fragments x0072 and x0354 as reference coordinates. (A) Ratio of stable atoms—an atom is determined as stable if the valence matches the expected valence for the element. (B) RMSD from reference—the calculated RMSD between the reference and sample, using an absolute one-to-one mapping ignores atom identity with low RMSD meaning molecules are similar to the reference and high RMSD they are not. (C) OpenEye measure Shapegauss—a Gaussian scoring function describing the shape fit between Mpro and samples, ignoring chemical interactions. A lower score means a better shape fit of the molecule. (D) Geometry stability—AIMNet geometry optimization was completed with Auto3D using the SMILES string of each sample. RMSD was calculated between the predicted geometry and the sampled geometry using RDKit. Horizontal lines indicate the sample median and circles indicate the sample mean.

Article Snippet: The agreement in the shape of the samples and the binding site of MPro were determined using the OpenEye toolkit version 2022.2.2 Gaussian scoring function Shapegauss., This scoring function measures the shape complementarity between the ligand and receptor by considering each heavy atom as a Gaussian function.

Techniques: Biomarker Discovery