semvec/pypi-packages huggingface dataset:
smrtnet-latest 0.25
A python lib for predicting small molecule-RNA interactions (SRIs)
License: MIT
Requires Python: ==3.8.10

Small molecules can bind RNAs to regulate their fate and functions, providing promising opportunities for treating human diseases. However, current tools for predicting small molecule-RNA interactions (SRIs) require prior knowledge of RNA tertiary structures, limiting their utility in drug discovery. Here, we present SMRTnet, a deep learning method to predict SRIs based on RNA secondary structure. By integrating large language models, convolutional neural networks, graph attention networks, and multimodal data fusion, SMRTnet achieves high performance across multiple experimental benchmarks, substantially outperforming existing state-of-the-art tools.
stm-sim 0.3.4
An STM simulation python library.
License: MIT

# stm_sim
 
An STM simulation python library from LDOS.

## Installation

pip install -U stm-sim

**Requirements**
* ASE
* surface_construct
* Mayavi (optional)

## Usage

```python
from stm_sim.stm import STM
parchg_file = "PARCHG"
stm = STM(bias=(-0.1, 0.1))
stm.read_parchg(parchg_file)
x, y, z = stm.scan(scan_mode='constant_current', repeat=(2, 2), plot=True)
```

**Parameters**

* `scan_mode`: default is `constant_current`, support `constant_height` and `constant_current`
* `repeat`: repeat the simulated image along x,y axes
* `plot`: plot it with matplotlib or not, default is `False`
* `startpoint`:  set the start point to find height can save time, used for `scan_mode='constant_current'`.
* `height`: 
  * The height for `scan_mode='constant_height'`. 
  * For `scan_mode='constant_current'`, it is for the height of tip to obtain the average current: h_tip = height + 0.76, 0.76 is the radius of carbon.
    * For `bottom=True`, it use old `get_avg_current_from_height`.
* `bottom`: upper or bottom side of slab, default is `False`


## TODO
* Export 2d data for external program plot.
* Generate INCAR for vasp task
* Support other program, like cp2k, fireball, fhi-aims, integrate multiwfn
* Use api design
* Autofocus method: height auto choose, current auto choose, bias auto choose
  * 使用傅立叶变换，目标是主频频率需要满足特定的规则，需要扩包；黑白要分明，最好对半分；高度差要在一个范围里面，用户可定义
  * 使用强化学习的方法，训练模型
  * 可能得到多个不同的图样，返回最大差异的图样
  * 默认频率的范围，用户可以手动确认
* 支持在台阶处的特殊处理