python scripts/generate_schematic.py "Graphical abstract for [paper title]: [brief description showing workflow from input → methods → key findings → conclusions]" -o figures/graphical_abstract.png

python scripts/generate_schematic.py "your diagram description" -o figures/output.png

python scripts/generate_image.py "your image description" -o figures/output.png

- Background: AI in drug discovery gaining traction
  * Cite recent reviews (Smith 2023, Jones 2024)
  * Traditional methods are slow and expensive
- Gap: Limited application to rare diseases
  * Only 2 prior studies (Lee 2022, Chen 2023)
  * Small datasets remain a challenge
- Our approach: Transfer learning from common diseases
  * Novel architecture combining X and Y
- Study objectives: Validate on 3 rare disease datasets

Artificial intelligence approaches have gained significant traction in drug discovery 
pipelines over the past decade (Smith, 2023; Jones, 2024). While these computational 
methods show promise for accelerating the identification of therapeutic candidates, 
traditional experimental approaches remain slow and resource-intensive, often requiring 
years of laboratory work and substantial financial investment. However, the application 
of AI to rare diseases has been limited, with only two prior studies demonstrating 
proof-of-concept results (Lee, 2022; Chen, 2023). The primary obstacle has been the 
scarcity of training data for conditions affecting small patient populations. 

To address this challenge, we developed a transfer learning approach that leverages 
knowledge from well-characterized common diseases to predict therapeutic targets for 
rare conditions. Our novel neural architecture combines convolutional layers for 
molecular feature extraction with attention mechanisms for protein-ligand interaction 
modeling. The objective of this study was to validate our approach across three 
independent rare disease datasets, assessing both predictive accuracy and biological 
interpretability of the results.

% Key findings (blue) - for major discoveries
\begin{keyfindings}[Title]
Content with key findings and statistics.
\end{keyfindings}

% Methodology (green) - for methods highlights
\begin{methodology}[Study Design]
Description of methods and procedures.
\end{methodology}

% Recommendations (purple) - for action items
\begin{recommendations}[Clinical Implications]
\begin{enumerate}
    \item Specific recommendation 1
    \item Specific recommendation 2
\end{enumerate}
\end{recommendations}

% Limitations (orange) - for caveats and cautions
\begin{limitations}[Study Limitations]
Description of limitations and their implications.
\end{limitations}

\begin{table}[htbp]
\centering
\caption{Results Summary}
\begin{tabular}{@{}lccc@{}}
\toprule
\textbf{Variable} & \textbf{Treatment} & \textbf{Control} & \textbf{p} \\
\midrule
Outcome 1 & \meansd{42.5}{8.3} & \meansd{35.2}{7.9} & <.001\sigthree \\
\rowcolor{tablealt} Outcome 2 & \meansd{3.8}{1.2} & \meansd{3.1}{1.1} & .012\sigone \\
Outcome 3 & \meansd{18.2}{4.5} & \meansd{17.8}{4.2} & .58\signs \\
\bottomrule
\end{tabular}

{\small \siglegend}
\end{table}

\documentclass[11pt,letterpaper]{report}
\usepackage{scientific_report}

\begin{document}
\makereporttitle
    {Report Title}
    {Subtitle}
    {Author Name}
    {Institution}
    {Date}

% Your content with professional formatting
\end{document}

xelatex report.tex