---
title: "AI-Enhanced Virtual Screening Identifies Selective Small-Molecule Ligands of SV2C"
id: "biorxiv-15-discovery-of-selective-small-molecule-ligands-of-sv2c-by-ai-enhanced-virtual"
canonical_url: "https://medichelpline.com/clinical-feed/biorxiv-15-discovery-of-selective-small-molecule-ligands-of-sv2c-by-ai-enhanced-virtual"
content_type: "clinical_feed_article"
specialty: "Pharmacology"
source_name: "bioRxiv (Biomedical Preprints)"
source_url: "https://www.biorxiv.org/content/10.64898/2026.08.11.744237v1?rss=1"
published_at: "2026-08-16T12:00:00.000Z"
evidence_level: "Verified Feed"
license: "CC-BY-NC-4.0 / Informational Use"
---
# AI-Enhanced Virtual Screening Identifies Selective Small-Molecule Ligands of SV2C
## Provenance & Clinical Metadata
- **Canonical URL:** https://medichelpline.com/clinical-feed/biorxiv-15-discovery-of-selective-small-molecule-ligands-of-sv2c-by-ai-enhanced-virtual
- **Specialty:** [Pharmacology](https://medichelpline.com/clinical-feed/pharmacology.md)
- **Primary Source:** bioRxiv (Biomedical Preprints)
- **Source URL:** [Original Journal Publication](https://www.biorxiv.org/content/10.64898/2026.08.11.744237v1?rss=1)
- **Published At:** 2026-08-16T12:00:00.000Z
- **Evidence Rating:** Verified Feed
## Executive GIST (TL;DR)
- Synaptic vesicle glycoprotein 2C (**SV2C**) is enriched in dopaminergic neurons of the basal ganglia, modulates dopamine storage and release, and is implicated in Parkinson’s disease (PD); no selective small-molecule probes existed prior to this work. - The authors used an **AI-enhanced virtual screening** workflow on a 5.96 million compound commercial library, filtering to 3.19 million CNS-relevant molecules before docking and rescoring with a convolutional neural network scoring function (**CNN_VS**). - A homology model of SV2C was built using SV2A cryo-EM structures as templates; the model’s conformational landscape was characterized by molecular dynamics (MD) and Gaussian accelerated MD (GaMD) simulations in both apo form and bound to known SV2 ligands (plosaracetam, levetiracetam, brivaracetam, padsevonil). - The **CNN_VS** had retrospective validation on an SV2A benchmark (r = 0.72 vs experimental pIC50) and was applied in a multi-stage funnel to prioritize candidates. - From 94 virtual-screening prioritized compounds, 71 were tested experimentally in an orthogonal primary assay cascade combining a thermal shift assay (TSA) and a [3H]-padsevonil scintillation proximity assay (SPA), followed by Ki determination and isoform selectivity profiling. - Experimental profiling yielded 22 active molecules (31% hit rate); hits segregated into two categories: padsevonil-site competitors and non-competitors. A subset of competitors also produced thermostabilization. - Two leads, compound 36 and compound 56, had Ki values at **SV2C** of 24.6 µM and 3.25 µM, respectively, each showing >10-fold selectivity over SV2A; compound 56 also showed ~12-fold selectivity versus SV2B. - Docking suggests a common binding mode anchored by conserved tryptophan residues (a “tryptophan cage”) in the SV2 pocket. An unpublished SV2A–plosaracetam cryo-EM structure independently confirmed the model (0.76 Å binding-site Cα RMSD) and conservation of this motif. - Subtle differences in SV2 luminal domains and transmembrane regions likely underlie isoform selectivity. The identified hit series provide starting points for hit-to-lead optimization and for tools to probe SV2C biology and its role in PD. - The work demonstrates that an integrated **AI-driven virtual screening** pipeline combined with medium-throughput biophysical assays can discover selective ligands for a structurally under-characterized membrane target.
## Clinical Analysis & Structured Key Points
Discovery of Selective Small-Molecule Ligands of SV2C by AI-Enhanced Virtual Screening and Experimental Validation | bioRxiv Skip to main content New Results Discovery of Selective Small-Molecule Ligands of SV2C by AI-Enhanced Virtual Screening and Experimental Validation View ORCID Profile Alexander C. Brueckner , View ORCID Profile Matthew F. Martin , View ORCID Profile Sheenam Khuttan , View ORCID Profile Benjamin Shields , View ORCID Profile Anshumali Mittal , View ORCID Profile Jennifer A. Schreiber , View ORCID Profile Romelia Salomon-Ferrer , View ORCID Profile Andrea Bortolato , View ORCID Profile Ali Salahpour , View ORCID Profile Meghan L. Bucher , View ORCID Profile Jonathan A. Coleman , View ORCID Profile Gary W. Miller doi: https://doi.org/10.64898/2026.08.11.744237 Alexander C. Brueckner 1 SandboxAQ, Palo Alto, California, United States; Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Alexander C. Brueckner For correspondence: alex.brueckner{at}sandboxaq.com Matthew F. Martin 2 Department of Structural Biology, University of Pittsburgh, Pittsburgh, Pennsylvania, United States; Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Matthew F. Martin Sheenam Khuttan 1 SandboxAQ, Palo Alto, California, United States; Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Sheenam Khuttan Benjamin Shields 1 SandboxAQ, Palo Alto, California, United States; Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Benjamin Shields Anshumali Mittal 2 Department of Structural Biology, University of Pittsburgh, Pittsburgh, Pennsylvania, United States; Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Anshumali Mittal Jennifer A. Schreiber 1 SandboxAQ, Palo Alto, California, United States; Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Jennifer A. Schreiber Romelia Salomon-Ferrer 1 SandboxAQ, Palo Alto, California, United States; Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Romelia Salomon-Ferrer Andrea Bortolato 1 SandboxAQ, Palo Alto, California, United States; Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Andrea Bortolato Ali Salahpour 3 University of Toronto, Toronto, Ontario, Canada; Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Ali Salahpour Meghan L. Bucher 4 Columbia University Irving Medical Center, New York, New York, United States Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Meghan L. Bucher Jonathan A. Coleman 2 Department of Structural Biology, University of Pittsburgh, Pittsburgh, Pennsylvania, United States; Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Jonathan A. Coleman Gary W. Miller 4 Columbia University Irving Medical Center, New York, New York, United States Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Gary W. Miller Abstract Info/History Metrics Supplementary material Preview PDF Abstract Synaptic vesicle glycoprotein 2C (SV2C) is a vesicular protein enriched in dopaminergic neurons of the basal ganglia that modulates dopamine storage and release, and its disruption is implicated in Parkinson's disease (PD). Despite strong genetic and pathological links to PD, there are no selective small-molecule probes for SV2C. Here, we describe an AI-enhanced virtual screening (VS) and experimental campaign that identified multiple novel chemotypes with low-micromolar affinity and marked selectivity for SV2C over SV2A and SV2B, starting from a large, general-purpose commercial library. Because no full-length high-resolution SV2C structure was available, we built a homology model using SV2A cryo-EM structures as templates and characterized its conformational landscape by molecular dynamics (MD) and Gaussian accelerated MD (GaMD) simulations in apo form and in complex with known SV2 ligands (plosaracetam, levetiracetam, brivaracetam, and padsevonil). A convolutional neural network-based scoring function (CNN_VS), retrospectively validated on a manually curated 39-ligand SV2A benchmark (r = 0.72 vs experimental pIC50), was then applied in a multi-stage funnel to 5.96 million Mcule in-stock compounds, which were sequentially filtered to 3.19 million CNS-relevant molecules before docking and rescoring. From 94 VS-prioritized candidates, 71 compounds were experimentally profiled in an orthogonal primary assay cascade combining a thermal shift assay (TSA) with a [3H]-padsevonil scintillation proximity assay (SPA), followed by Ki determination and isoform selectivity profiling for key hits. This campaign yielded 22 active molecules (31% hit rate) that naturally segregated into two categories: compounds that showed primary site competition, and compounds that did not show primary site competition with [3H]-padsevonil. A subset of competitor compounds also showed thermostabilization activity. Among these, compounds 36 and 56 emerged as particularly attractive leads, with Ki values of 24.6 uM and 3.25 uM at SV2C, respectively, and greater than 10-fold selectivity versus SV2A; compound 56 also maintained approximately 12-fold selectivity relative to SV2B. A complementary subset of SV2C-selective hits behaved as padsevonil-site competitors, providing a lead set that will serve as a template for functional characterization and future drug development for conditions that affect dopaminergic signaling. Docking analysis suggests a common binding mode anchored by conserved tryptophan residues in the SV2 pocket, a prediction independently confirmed by an unpublished SV2A-plosaracetam cryo-EM structure showing 0.76 Angstrom binding-site C-alpha RMSD relative to the SV2C model and complete conservation of the tryptophan cage. Subtle differences in the luminal domain and transmembrane region point to the structural determinants underlying isoform selectivity. Collectively, these results demonstrate that an AI-driven VS pipeline, tightly integrated with medium-throughput biophysical assays, can deliver selective SV2C binders from a general chemical library on a structurally under-characterized membrane target. The identified hits provide multiple starting points for hit-to-lead optimization and tools for probing SV2C biology and its role in PD. Competing Interest Statement A.C.B., S.K., B.S., J.A.S., R.S.F., and A.B. are employees of SandboxAQ. The AQFEP method used in this work is the subject of a SandboxAQ patent. The remaining authors declare no competing interests. Funder Information Declared National Institutes of Health , ES023839 SPARK NS Translational Research Program , N/A Copyright The copyright holder for this preprint is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under a CC-BY-NC-ND 4.0 International license . Back to top Previous Next Posted August 16, 2026. Download PDF Supplementary Material Email Thank you for your interest in spreading the word about bioRxiv. NOTE: Your email address is requested solely to identify you as the sender of this article. Your Email * Your Name * Send To * Enter multiple addresses on separate lines or separate them with commas. You are going to email the following Discovery of Selective Small-Molecule Ligands of SV2C by AI-Enhanced Virtual Screening and Experimental Validation Message Subject (Your Name) has forwarded a page to you from bioRxiv Message Body (Your Name) thought you would like to see this page from the bioRxiv website. Your Personal Message CAPTCHA This question is for testing whether or not you are a human visitor and to prevent automated spam submissions. Share Discovery of Selective Small-Molecule Ligands of SV2C by AI-Enhanced Virtual Screening and Experimental Validation Alexander C. Brueckner , Matthew F. Martin , Sheenam Khuttan , Benjamin Shields , Anshumali Mittal , Jennifer A. Schreiber , Romelia Salomon-Ferrer , Andrea Bortolato , Ali Salahpour , Meghan L. Bucher , Jonathan A. Coleman , Gary W. Miller bioRxiv 2026.08.11.744237; doi: https://doi.org/10.64898/2026.08.11.744237 Share This Article: Copy Citation Tools Discovery of Selective Small-Molecule Ligands of SV2C by AI-Enhanced Virtual Screening and Experimental Validation Alexander C. Brueckner , Matthew F. Martin , Sheenam Khuttan , Benjamin Shields , Anshumali Mittal , Jennifer A. Schreiber , Romelia Salomon-Ferrer , Andrea Bortolato , Ali Salahpour , Meghan L. Bucher , Jonathan A. Coleman , Gary W. 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