Personal agent for highlighting interesting words

MolSnapper: Conditioning Diffusion for Structure Based Drug Design

“…Additionally, clashes with the protein were observed due to SILVR’s model not incorporating protein information. This is further compounded by the fact that our base model, MolDiff 15 , surpasses the base model that was used for SILVR, EDM 9 , primarily because it models and diffuses the bonds of the molecule, resulting in the generation of molecules with better validity and Synthetic Accessibility 40 .…”

“…As our base model we use MolDiff 15 as it is among the top-performing models for molecule generation 15 . MolDiff was pre-trained on the GEOM-Drug dataset 31 .…”

“…In the context of the diffusion model framework, following the framework introduced in the MolDiff paper 15 , during the reverse process, the Markov chain is reversed to reconstruct the true sample. This involves using E (3)-equivariant neural networks to parameterize the transition p θ ( M t− 1 | M t ) from prior distributions, where and .…”

“…To address the limitations of autoregressive models, recent studies 9,15–18 have turned to diffusion models 19 . These models iteratively denoise data points sampled from a prior distribution to generate samples.…”

“…The limited volume of experimentally determined structures of protein-ligand complexes often leads models to learn dataset biases rather than grasping the true biophysical principles underlying ligand-protein interactions 21 . Diffusion methods have also been developed and trained on the far larger dataset of just molecules which allows better coverage of drug-like space potentially leading to more viable and synthesizable drug candidates 15 .…”