PocketVec

Closed weights Barcelona Institute of Science and Technology,Universitat de Barcelona,Institució Catalana de Recerca i Estudis Avançats (ICREA) March 2024

No estimate

No hardware requirements for this model

The weights for this model have not been published, so it cannot be downloaded or run on your own hardware at any size. It is reachable only through its provider, and no graphics card changes that.

On record

Full specification

Everything on record for this model. Most of it describes how it was trained rather than how it runs — useful context for judging how much work went into it, and how it compares with models built at a different scale.

Origin

Who built this model, where, and when it was published.

Organisation
Barcelona Institute of Science and Technology,Universitat de Barcelona,Institució Catalana de Recerca i Estudis Avançats (ICREA)
Organisation type
Academia
Country
Spain
Published
16 March 2024
Authors
Arnau Comajuncosa-Creus, Guillem Jorba, Xavier Barril, Patrick Aloy

What it does

The problem areas the model was built for. A model can carry several of each.

Domain
Biology
Task
Drug discovery

Size

How large the model is and how much data it was trained on. Parameters are the figure that decides whether it fits on a given graphics card.

Training data
tokens

0.0 Final calculation: 0.0 (No additions or multiplications performed as there is no training data involved)

Availability

Whether you can obtain the model and run it on your own hardware, which is what decides if any of the graphics-card figures on this page apply.

Weights
Closed — provider access only
Model access
Unreleased
Training code
Open (non-commercial)

no clear license https://gitlabsbnb.irbbarcelona.org/acomajuncosa/pocketvec "The PocketVec Repository holds the code needed to create a PocketVec descriptor for any protein binding site of interest together with all results presented along the manuscript."

How it is classified

Labels the source dataset applies when tracking notable models, and how confident it is in the entry.

Record confidence
Unknown

Sources

Where this record came from and when it was last checked.

Reference
Comprehensive detection and characterization of human druggable pockets through binding site descriptors
Last updated
28 November 2025

What the numbers mean

About this model

PocketVec was published by Barcelona Institute of Science and Technology,Universitat de Barcelona,Institució Catalana de Recerca i Estudis Avançats (ICREA), in Spain, in March 2024. The organisation is categorised as academia.

It works in Biology, and is recorded as doing drug discovery.

Its weights were never published, so it can only be reached through its provider. No graphics card changes that.

Answers

PocketVec — common questions

01

When was PocketVec released?

PocketVec was published in March 2024. Capability per parameter has improved considerably since, so a newer model of the same size is often the better use of the same hardware.

02

What is PocketVec used for?

PocketVec works in Biology, and is recorded as handling drug discovery. These are the areas it was designed around; they describe intent rather than a hard boundary.

03

What GPU do I need to run PocketVec?

None. PocketVec is a closed model — its weights were never published, so it cannot be downloaded or run on your own hardware at any price. It is reachable only through its provider.

04

Is PocketVec open source?

No. PocketVec has not had its weights published, so it exists only as a service controlled by its owner.

05

How many parameters does PocketVec have?

No parameter count has been published for PocketVec, which is why no memory or speed figure appears on this page.

06

Who created PocketVec?

PocketVec was published by Barcelona Institute of Science and Technology,Universitat de Barcelona,Institució Catalana de Recerca i Estudis Avançats (ICREA), based in Spain, categorised as academia.

Source

Original publication

Record last updated 28 November 2025

The other direction

Looking at it from the other side?

This page starts from the model. If you already own a card and want to know everything it will run, start from the hardware instead.