semvec/pypi-packages huggingface dataset:
prosper-nn 0.3.2
Package contains, in PyTorch implemented, neural networks with problem specific pre-structuring architectures and utils that help building and understanding models.
Requires Python: >=3.6

# Prosper_NN

## Problem-Specific Pre-Structuring of Neural Networks

Accurate data-driven forecasts can provide a crucial advantage in many application areas. One of the methods with the most promising results in forecasting time series are neural networks. However, especially in macro-economic applications, it can be difficult and time-consuming to adapt state-of-the-art neural network architectures in a way that leads to satisfying results. For instance, the final prices of materials and stocks result from a highly complex interplay between supply and demand. Additionally, there is often only one (albeit long) historical time series available for training which makes correlations in the data difficult to detect.

Under these circumstances, applying state-of-the-art neural networks architectures successfully poses a great challenge. Pre-structuring the models can solve this problem. For this purpose, Zimmermann, Tietz and Grothmann (Neural Networks: Tricks of the Trade, 2012) propose recurrent architectures for various time series problems that help recognize correlations. They recommend Error-Correction Neural Networks (ECNNs), Historical-Consistent Neural Networks (HCNNs) and Causal-Retro-Causal Neural Networks (CRCNNs). One of the main ideas of the pre-structuring is embedding the model in a larger architecture in order to use the past prediction errors for predicting the next time step. The three approaches mentioned use this idea and apply it in different settings. So far, the proposed architectures are not publicly available in common machine learning frameworks. Therefore, we have implemented the models in PyTorch. This way, we can easily test them on diverse datasets.
In this package the neural network architectures developed by Hans-Georg Zimmermann are implemented in PyTorch.
The full documentation can be found here https://iis-scs-a.pages.fraunhofer.de/prosper/prosper/. There are also tutorials that show how to work with the package.
DockEva 1.0.0
Calculating the Docking RMSD between two ligands
Requires Python: >=3.7

# DockEva
DockEva: a fast open-source tool for atom mapping and docking evaluation of symmetric small molecules

Correctly comparing two poses of small molecule ligands with symmetry in fast is of significance important for the development of the related fields of receptor-ligand docking. Most of existing tools that correct for molecular symmetry do not consider the bonding structure of molecules, therefore resulting in non-physical atomic mapping. Using exhaustive algorithm to directly solve high sym-metry atom mapping problem is time-consuming. We present DockEva, a fast method for docking pose evaluation that converts the symmetry atom mapping problem to multiple maximum clique problems using the shortest road algorithm, in which the optimal atomic mapping and docking pose evaluation are performed in fast by solving the multiple maximum clique problems. Benchmarked results on decoys generated by AutoDock Vina demonstrate that DockEva can efficiently achieve the symmetry-corrected atom mapping with the minimum root mean square deviation (RMSD). Compared DockRMSD, one of the best existing methods for calculating RMSD of symmetric molecules, the average computational efficiency of DockEva (4.78×10<sup>-3</sup> seconds) is improved by about 6.36×10<sup>2</sup> times on the highly symmetric small molecules (their reasonable atom mapping numbers are all larger than 100) with more robust.

## Pre-requisite:
    - python

## Installation:
### On Linux/MacOS system:

~~~
  $ pip install DockEva
~~~
### On Windows system:

~~~
  $ pip install DockEva
~~~
## Example usage:
~~~
  $ DockEva path/to/crystal.mol2 path/to/decoy.mol2
~~~
For more example usages and options, try
~~~
  $ DockEva -h
~~~

## Example output:

Check https://zhanglab.comp.nus.edu.sg/DockEva/ for an example output

## Release History:

- First release          2024-10-14

## Tips

* <b>This package is free for all users</b>. If you have any question, please email Jun Hu: junh_cs@126.com

## References
[1] Jun Hu, Jiawen Li, Yang Zhang. DockEva: a fast open-source tool for atom mapping and docking evaluation of highly symmetric molecules. sumitted.