8 Best papers-for-molecular-design-using-DL Alternatives in 2026 (Open Source)

papers-for-molecular-design-using-DL — List of Molecular and Material design using Generative AI and Deep Learning . The most comprehensive and continuously-updated curated collection of AI-for-molecular-design papers on GitHub, organized by deep learning methodology — serving as the de facto entry point for researchers in this interdisciplinary field

These 8 open-source tools do the same job. They are ordered by how closely they match papers-for-molecular-design-using-DL, with live GitHub data so you can see which projects are actively maintained.

1. 1. LLaMA-Cult-and-More

   Large Language Models for All, 🦙 Cult and More, Stay in touch !

   What sets it apart: vs Awesome-LLM / Papers With Code: practitioner-oriented catalog with detailed model specs (parameters, training data, license), alignment post-training guides, and efficient fine-tuning technique references in a single document

   Best for: Researchers tracking the open-source LLM landscape and model lineages; Practitioners comparing model sizes, licenses, and training data; Anyone needing a curated starting point for LLM fine-tuning datasets and techniques

   ⭐ 447papers-for-molecular-design-using-DL vs LLaMA-Cult-and-More →Full profile →

2. 2. AI Collection

   The Generative AI Landscape - A Collection of Awesome Generative AI Applications

   What sets it apart: vs Product Hunt/alternatives.to: focused exclusively on generative AI with 4172+ apps across 43 categories, community-maintained

   Best for: Discovering AI applications across diverse categories; Market research on the generative AI landscape

   ⭐ 9.2kpapers-for-molecular-design-using-DL vs AI Collection →Full profile →

3. 3. Awesome Best of AI

   A curated list of best ai tools

   What sets it apart: vs ai-collection/awesome lists: focuses exclusively on actively-maintained, widely-adopted tools with quality curation across 8 AI categories

   Best for: Discovering top AI tools across diverse categories; Market research on the actively-maintained AI landscape

   ⭐ 731papers-for-molecular-design-using-DL vs Awesome Best of AI →Full profile →

4. 4. AI Directories

   An awesome list of best top AI directories to submit your ai tools

   Best for: AI product creators seeking distribution channels for visibility; Entrepreneurs looking for platforms to list their AI tools; Marketers building multi-platform AI product launch strategies

   ⭐ 881papers-for-molecular-design-using-DL vs AI Directories →Full profile →

5. 5. Open LLMs

   📋 A list of open LLMs available for commercial use.

   What sets it apart: The go-to curated reference for commercially-licensable open LLMs with clear license tracking — a simple but essential resource vs model hubs that don't filter by commercial usability

   Best for: Teams evaluating which open-source LLMs are safe for commercial use; Researchers tracking the landscape of openly available language models

   ⭐ 12.9kpapers-for-molecular-design-using-DL vs Open LLMs →Full profile →

6. 6. LLM-eval-survey

   The official GitHub page for the survey paper "A Survey on Evaluation of Large Language Models".

   What sets it apart: Comprehensive survey and curated collection of LLM evaluation papers and resources organized by what, where, and how to evaluate

   Best for: llm-evaluation-research; finding-evaluation-benchmarks; understanding-eval-landscape

   ⭐ 1.6kpapers-for-molecular-design-using-DL vs LLM-eval-survey →Full profile →

7. 7. Generative AI on Google Cloud

   Sample code and notebooks for Generative AI on Google Cloud, with Gemini on Vertex AI

   What sets it apart: Google's official sample repository for Generative AI on Google Cloud — the most comprehensive collection of Gemini, Imagen, and Vertex AI notebooks, unlike third-party tutorials it's maintained by Google and always reflects latest APIs

   Best for: Learning Google Cloud's generative AI capabilities with hands-on examples; Teams already on Google Cloud wanting to integrate Gemini/Vertex AI

   ⭐ 17.8kpapers-for-molecular-design-using-DL vs Generative AI on Google Cloud →Full profile →

8. 8. Anthropic courses

   Anthropic's educational courses

   What sets it apart: vs generic prompt engineering guides: official Anthropic courses with hands-on Claude API exercises, covering fundamentals through production evaluation

   Best for: Developers learning Anthropic Claude API from scratch; Teams establishing prompt engineering best practices

   ⭐ 22.9kpapers-for-molecular-design-using-DL vs Anthropic courses →Full profile →