Update README.md

README.md

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 **PepMLM: Target Sequence-Conditioned Generation of Peptide Binders via Masked Language Modeling**
 
-Target proteins that lack accessible binding pockets and conformational stability have posed increasing challenges for drug development. 
-Induced proximity strategies, such as PROTACs and molecular glues, have thus gained attention as pharmacological alternatives, but still require small molecule docking at binding pockets for targeted protein degradation (TPD). 
-The computational design of protein-based binders presents unique opportunities to access “undruggable” targets, but have often relied on stable 3D structures or predictions for effective binder generation. 
-Recently, we have leveraged the expressive latent spaces of protein language models (pLMs) for the prioritization of peptide binders from sequence alone, which we have then fused to E3 ubiquitin ligase domains, creating a CRISPR-analogous TPD system for target proteins. 
-However, our methods rely on training discriminator models for ranking heuristically or unconditionally-derived “guide” peptides for their target binding capability. 
-
 In this work, we introduce **PepMLM**, a purely target sequence-conditioned *de novo* generator of linear peptide binders. 
 By employing a novel masking strategy that uniquely positions cognate peptide sequences at the terminus of target protein sequences, 
 PepMLM tasks the state-of-the-art ESM-2 pLM to fully reconstruct the binder region,