This repository contains the LoRA adapter weights for the protein encoder used in BindScreen, trained on filtered ChEMBL. BindScreen is a sequence-based virtual screening method built on a dual-encoder contrastive architecture. The adapter fine-tunes ESM2 T36 on protein-molecule interaction task.

The projection layers are available separately at SaeedLab/BindScreen-Finetuning, which also contains the full model description, architecture diagram, and usage examples.

[Github Repo] | [Dataset on HuggingFace] | [Model Collection] | [Cite]

Virtual screening aims to identify candidate molecules that bind to a target protein, playing a central role in computational drug discovery. Sequence-based deep learning methods offer a more broadly applicable alternative to structure-based approaches, since they do not require 3D structural information. However, they typically require a separate forward pass per protein-molecule pair, limiting their scalability to large molecular libraries. Contrastive learning methods inspired by CLIP address this by encoding proteins and molecules independently, allowing similarity analysis via simple comparisons rather than a forward pass per pair. However, standard CLIP training was designed for symmetric tasks and does not account for the asymmetric and one-to-many nature of protein-molecule binding. In this paper, we introduce BindScreen, a sequence-based virtual screening method built on a dual-encoder contrastive architecture. BindScreen introduces a protein-centric batch construction strategy and an asymmetric multi-positive InfoNCE loss to cope with the protein-centric nature of virtual screening. We conducted a systematic evaluation of 8 protein language models and 3 molecular language model variants against BindScreen. The proposed protein-centric batch construction consistently outperforms standard CLIP training across all evaluated encoders while substantially improving computational efficiency, reducing training cost by up to 32 times. In addition, our experiments demonstrate that BindScreen requires 7 times fewer inference computations than pairwise virtual screening approaches. On the LIT-PCBA dataset, BindScreen outperforms all sequence-based baselines, achieving a relative improvement of up to 39% in EF at 0.5 over the best competing method, while remaining competitive with traditional docking approaches without requiring 3D structural information.

This adapter corresponds to the BindScreen-Finetuning configuration, in which ESM2 T36 is fine-tuned via LoRA alongside the projection layers. Two configurations are available in this collection:

This adapter must be used together with SaeedLab/BindScreen-Finetuning, which provides the projection layers. The full usage example, including molecule encoding and similarity computation, is available in that repository.

The paper is under review. As soon as it is accepted, we will update this section.

This model and associated code are released under the CC-BY-NC-ND 4.0 license and may only be used for non-commercial, academic research purposes with proper attribution. Any commercial use, sale, or other monetization of this model and its derivatives, which include models trained on outputs from the model or datasets created from the model, is prohibited and requires prior approval. Downloading the model requires prior registration on Hugging Face and agreeing to the terms of use. By downloading this model, you agree not to distribute, publish or reproduce a copy of the model. If another user within your organization wishes to use the model, they must register as an individual user and agree to comply with the terms of use. Users may not attempt to re-identify the deidentified data used to develop the underlying model. If you are a commercial entity, please contact the corresponding author.

For any additional questions or comments, contact Fahad Saeed (fsaeed@fiu.edu).