ChemicalQDevice
ChemicalQDevice
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mAb Bioprocess Engineering In Context Table Forecasts using Conversational AI Literature Insights
Cite this work.
Kawchak K. mAb Bioprocess Engineering In-Context Table Forecasts using Conversational AI Literature Insight Generations. ChemRxiv. 2024; doi:10.26434/chemrxiv-2024-jzbj0 This content is a preprint and has not been peer-reviewed. doi.org/10.26434/chemrxiv-2024-jzbj0
Переглядів: 37

Відео

Monoclonal Antibody Bioprocess Engineering Advancements Using Conversational Artificial Intelligence
Переглядів 40Місяць тому
Cite this work. Kawchak K. Monoclonal Antibody Bioprocess Engineering Advancements Using Conversational Artificial Intelligence. ChemRxiv. 2024; doi:10.26434/chemrxiv-2024-3m7m1 This content is a preprint and has not been peer-reviewed. doi.org/10.26434/chemrxiv-2024-3m7m1
Paclitaxel Biosynthesis AI Breakthrough
Переглядів 5112 місяці тому
Cite this work. Kawchak K. Paclitaxel Biosynthesis AI Breakthrough. ChemRxiv. 2024; doi:10.26434/chemrxiv-2024-pqjd3 This content is a preprint and has not been peer-reviewed. doi.org/10.26434/chemrxiv-2024-pqjd3
High Dimensional and Complex Spectrometric Data Analysis of an Organic Compound using LMMs
Переглядів 453 місяці тому
Cite this work. Kawchak K. High Dimensional and Complex Spectrometric Data Analysis of an Organic Compound using Large Multimodal Models and Chained Outputs. ChemRxiv. 2024; doi:10.26434/chemrxiv-2024-06gf1 This content is a preprint and has not been peer-reviewed. doi.org/10.26434/chemrxiv-2024-06gf1
LMM Spectrometric Determination of an Organic Compound
Переглядів 393 місяці тому
Cite this work. Kawchak K. LMM Spectrometric Determination of an Organic Compound. ChemRxiv. 2024; doi:10.26434/chemrxiv-2024-qtnkj This content is a preprint and has not been peer-reviewed. doi.org/10.26434/chemrxiv-2024-qtnkj
LMM Chemical Research with Document Retrieval
Переглядів 724 місяці тому
Cite this work. Kawchak K. LMM Chemical Research with Document Retrieval. ChemRxiv. 2024; doi:10.26434/chemrxiv-2024-p91gm This content is a preprint and has not been peer-reviewed. doi.org/10.26434/chemrxiv-2024-p91gm
Total Synthesis Guidance for Chemists using LLM LMM Prompts
Переглядів 2064 місяці тому
Cite this work. Kawchak K. Total Synthesis Guidance for Chemists. Zenodo. 2024; doi:10.5281/zenodo.13273141 This content is a preprint and has not been peer-reviewed. zenodo.org/records/13754637 GitHub: Link to file. github.com/kevinkawchak/Generative-AI-Drug-Synthesis/tree/845db1372aeec5236bd5edf551a677a99df78949/Code/Drug Discovery/LMM
Seminar: Drug Synthesis and 2024 LLM Inferencing
Переглядів 4094 місяці тому
2024 LLMs: Faster than PhD level chemists, but quality across many prompts needs improvement. A) Many low cost/no cost LLMs with emergent properties exist for developing new medicine. B) Explainability Matters - some generations reveal a complete summary of their findings. Document, page number, selection of text, and line number IDs need improvement. Subsequent synthetic chemistry steps improv...
Can LLMs Help Researchers Automate Individual Disease Drug Design?
Переглядів 1075 місяців тому
PDF: drive.google.com/file/d/1SO9hf3TtSOXjCNG3rY7r06pbO_s2dBMo/view?usp=sharing Large Language Models (LLMs) are improving existing drug discovery AI tasks with advanced functionalities to facilitate molecular simulation and reaction prediction. As a central point, LLMs can interact with public databases, and also use reinforcement learning combined with smaller models and high throughput scree...
Myriad LLM Approaches to Drug Discovery Generative Artificial Intelligence
Переглядів 1915 місяців тому
PDF: drive.google.com/file/d/1heg0iLPUOb6lcXLcg491-_cgRb99NsRh/view?pli=1 The time has come for medicinal chemists with a wider range of skills to make a greater impact on the drug discovery process. This is accomplished through few sentence prompts to modern Large Language Models across many devices that generate informed responses based on vast amounts of chemical information. Stepwise buildi...
Speed-ups for State of the Art Generative AI Drug Discovery Applications
Переглядів 1475 місяців тому
Here, (2) 2024 Generative AI reviews for drug discovery and molecular design were summarized. This was followed by a 2024 paper featuring a transcriptional signatures generator variational autoencoder for a pancreatic cancer application. The authors’ model was found to be faster than several others tested. Hugging Face pipeline experiments were run as a rapid GenAI screening tool. In specific, ...
Live Python Demos - Generative Artificial Intelligence
Переглядів 3515 місяців тому
GitHub: github.com/kevinkawchak/Medical-Quantum-Machine-Learning/tree/main/Code/Generative AI Live/27Jun24_Python_OpenWebUI Hosting Generative AI Models on laptops is a reality for many developers in 2024. In specific, both Large Language Models (LLMs) and Large Multimodal Models (LMMs) were served on a MacBook Pro m3 max with 48GB. Users from across the country were permitted to upload an imag...
Live Coding: GenAI Multimodal, RAG, RAPTOR, Agent, and Finetuning
Переглядів 4526 місяців тому
Here, a total of 6 Generative AI demos were run and posted online: RAG, RAPTOR, Agent, and Finetuning Colab notebooks, as well as Multimodal image prompts given to OpenAI ChatGPT 4o and Google Gemini. Generations from notebooks typically reflected their desired purpose, with Finetuning being applied with a biochemistry structures dataset and tested with general and scientific questions, and the...
C++ Generative AI Inference: Production Ready Speed and Control
Переглядів 6026 місяців тому
PDF: www.chemicalqdevice.com/c-genai-speed-and-control C methods to improve speed and control of Generative AI inferencing will be covered. C is an object-oriented programming language which gives a clear structure to programs and allows code to be reused. (1) Generation below: Llama3 q4_0 4.34GB using on-device GPT4All v2.8.0 June 9th 2024. Built with Meta Llama 3. Prompt: What are all the way...
Apple On-Device GenAI MacOS, iOS: New Utility for Lead Optimization
Переглядів 1016 місяців тому
PDF: www.chemicalqdevice.com/apple-on-device-genai-macos-ios Lead Optimization in drug discovery is the process of further modifying compounds to optimize their potency, selectivity, and pharmacokinetic properties. This process has historically been computationally demanding, and in recent years has seen additional advancements with AI. According to a Gangwal, et al. 2024 paper, generative arti...
Local Generative AI Model Frameworks for Different Stages of Drug Discovery
Переглядів 2036 місяців тому
Local Generative AI Model Frameworks for Different Stages of Drug Discovery
How to Develop APIs for Generative AI Drug Discovery Production
Переглядів 2646 місяців тому
How to Develop APIs for Generative AI Drug Discovery Production
LangChain Suite for Advanced Drug Discovery Generative AI Workflows
Переглядів 1,2 тис.7 місяців тому
LangChain Suite for Advanced Drug Discovery Generative AI Workflows
Cancer Drug Discovery AI Agentic Workflow R&D
Переглядів 1,4 тис.7 місяців тому
Cancer Drug Discovery AI Agentic Workflow R&D
Meta Llama 3 Drug Discovery Generative AI Assistant - Developments
Переглядів 3,1 тис.7 місяців тому
Meta Llama 3 Drug Discovery Generative AI Assistant - Developments
Meta Llama 3 Fine tuning, RAG, and Prompt Engineering for Drug Discovery
Переглядів 18 тис.7 місяців тому
Meta Llama 3 Fine tuning, RAG, and Prompt Engineering for Drug Discovery
Drug Discovery Generative AI End to End De Novo Proteins; Tensor Networks
Переглядів 4878 місяців тому
Drug Discovery Generative AI End to End De Novo Proteins; Tensor Networks
Drug Discovery Generative AI using Tensor Network GPT or BERT
Переглядів 5988 місяців тому
Drug Discovery Generative AI using Tensor Network GPT or BERT
How to Re-Code LLMs Layer by Layer with Tensor Network Substitutions
Переглядів 2548 місяців тому
How to Re-Code LLMs Layer by Layer with Tensor Network Substitutions
LLM Explainability or Controllability Improvements with Tensor Networks
Переглядів 2378 місяців тому
LLM Explainability or Controllability Improvements with Tensor Networks
Tensor Networks vs. PCA and PLS for High Dimensional Medical Datasets
Переглядів 1278 місяців тому
Tensor Networks vs. PCA and PLS for High Dimensional Medical Datasets
Tensor Network, Neural Network, or Hybrid - AI Practical Use
Переглядів 2619 місяців тому
Tensor Network, Neural Network, or Hybrid - AI Practical Use
Tensor Network Developer Revolution: 125 Lines of Code or Directions
Переглядів 1 тис.9 місяців тому
Tensor Network Developer Revolution: 125 Lines of Code or Directions
FDA Good Machine Learning Practice Guidelines; and Practical QiML 2 0
Переглядів 919 місяців тому
FDA Good Machine Learning Practice Guidelines; and Practical QiML 2 0
QiML 2.0: Speed Ups, Scalability, and Performance for New Machine Learning Era
Переглядів 829 місяців тому
QiML 2.0: Speed Ups, Scalability, and Performance for New Machine Learning Era

КОМЕНТАРІ

  • @WarmupsLIVE
    @WarmupsLIVE 2 місяці тому

    cool!!

  • @TheZEN2011
    @TheZEN2011 2 місяці тому

    I like how you push things further with the tools. You are on The Cutting Edge! Thank you for sharing your research!

  • @SetTheCurve
    @SetTheCurve 4 місяці тому

    In my experience, GPT can’t even answer high school chemistry questions in a reliable way. So really what I took from this lecture is that GPT is able to more or less read an article for you and answer basic questions about its contents so long as you can specify.

    • @Azpalon
      @Azpalon 4 місяці тому

      I wish some programmer and chemist would combine their powers to build a chemistry simulator that GPT could hook into, enabling it to run tests and obtain accurate and comprehensive output data. It's theoretically possible, but of course, like anything cool and useful, it requires a lot of work to set up.

  • @madmen1986
    @madmen1986 5 місяців тому

    Another state of the art video, your channel is getting bigger every day. I clicked as soon as I saw this.

  • @vipulinaparty7798
    @vipulinaparty7798 5 місяців тому

    Great Session , Truly appreciate the flow and the content , was going through your github profile couldn't find jupyter notebook for RAPTOR module please let me know how to get it.

  • @ranchvidz9832
    @ranchvidz9832 6 місяців тому

    Another invaluable video. This channel provides the most forward thinking ML videos on UA-cam.

  • @streetjesus6846
    @streetjesus6846 6 місяців тому

    Really good stuff, thanks

  • @zapy422
    @zapy422 6 місяців тому

    how to join these talks live?

  • @ehza
    @ehza 6 місяців тому

    This is cool

  • @ranchvidz9832
    @ranchvidz9832 6 місяців тому

    These videos are a distillation of the most important concepts in ML. More and more people are becoming interested in your Company, ChemicalQdevice

  • @madmen1986
    @madmen1986 7 місяців тому

    Kevin, these in depth advanced videos are building your company, ChemicalQdevice, a powerful interconnected system of knowledge. The more videos you create, the stronger your Company and its influence will become.

  • @parasetamol6261
    @parasetamol6261 7 місяців тому

    can you give me research paper for this topic.

  • @bhanujinaidu
    @bhanujinaidu 7 місяців тому

    Good job 👍

  • @KumR
    @KumR 7 місяців тому

    10

  • @adriangpuiu
    @adriangpuiu 7 місяців тому

    one of the best videos I watched this week

  • @meelanc1203
    @meelanc1203 7 місяців тому

    Thank you for sharing the video. As you mentioned, it would be helpful to have the links to the associated Jupyter notebooks. Could you please provide those in the video description?

  • @HarveyCastroMD
    @HarveyCastroMD 7 місяців тому

    Strong work #DRGPT

  • @heythere6390
    @heythere6390 7 місяців тому

    Damn, this is good shit! Many thanks!

  • @harristengku7153
    @harristengku7153 7 місяців тому

    I dont even know chemistry and this is interesting

  • @dadsonworldwide3238
    @dadsonworldwide3238 7 місяців тому

    I'd love to get hold of tools I could simulate elements under critical extreme states and different lattus structures/body.. lol Hobby inventor of exotic materials is one area I can envision help in cutting cost on worthy testing even if it just rules out waist of time. Great work in explaining the usefulness. I'd expect this to be a very largely needed excersize. As a retired machinest, I've long been curious as to why some fields and disciplines have been so slow to innovate.. I get that many of them aren't conditioned for it even when methods to streamline existed its not necessarily any mechanism to stress it upon them the way it does for the elder industry born of innovation itself. Or they just developed in the old world and have no system within to condition themselves to be cutting edge.

  • @madmen1986
    @madmen1986 7 місяців тому

    Please keep posting these advanced yet practical videos. Whoever serves the community with knowledge that it is advanced, will attract the most users to their company, via the developer community.

  • @madmen1986
    @madmen1986 7 місяців тому

    Please keep posting these technical videos. So many people are tired of beginner level knowledge. Whoever serves the community with relevant, advanced knowledge will prosper.

  • @TheGenerationGapPodcast
    @TheGenerationGapPodcast 7 місяців тому

    This guy can make abc and 123 complex.

  • @simonmasters3295
    @simonmasters3295 7 місяців тому

    So what Kevin Kawchak is saying...(smile)... is that it doesn't matter much about which LLM model you use. All the LLM does is provide control of the conversation. What counts is a phase in which a bias is developed towards text-based current information which the developer or user provides to the LLM interface. Yyou cannot be assured that your data, or even the domain of interest, has been used to train the LLM model's response. Effectively, Retrieval Augmented Generation (RAG) patches in small, medium or large amounts of structured or unstructured data into to the AI environment and the AI provides answers accordingly using a vector database. This raises the question of how do we test accuracy? And that depends on whether the output is rigorously re-evaluated after the RAG process. I feel the need for a workflow...

  • @bamh1re318
    @bamh1re318 7 місяців тому

    Some great drug discoveries are based on small experiments, e.g., Tamoxifen, Gleevec, Crizotinib & Vemurafenib etc. Is massive "fine-tuning" of LLM necessary, or counter-productive vs specific/narrow training?

    • @simonmasters3295
      @simonmasters3295 7 місяців тому

      What do *you* think?

    • @bamh1re318
      @bamh1re318 7 місяців тому

      @@simonmasters3295 In the case of Greevec, it was discovered on RTK-cell models. Its effect on bcr-abl is a "also-found". It's potential was revealed in one single assay with CML patient's bone marrow culture. The other 3 compounds are in similar situation. Med-Chem or pharmacology was not the bottle neck. Instead our bias or visions were

    • @bamh1re318
      @bamh1re318 7 місяців тому

      @@simonmasters3295 Target-/process-driven AI models, which grow with discovery/development progression could be easier to put into practice.

  • @q-bra
    @q-bra 8 місяців тому

    Great Dr. Kevin.

  • @abhimohan240
    @abhimohan240 8 місяців тому

    Thank you for a wonderful discussion!

  • @Hugo-gv8qj
    @Hugo-gv8qj 8 місяців тому

    "Promo SM" 💪

  • @123string4
    @123string4 9 місяців тому

    I did my thesis in 2017 on tensor networks for modelling potential energy states of water molecules. I used the approach Oseledets 2011 highlights for dimensionality reduction. I'm not even in a STEM field anymore so I was surprised to find just how popular they are now.

  • @michealmcpherson1575
    @michealmcpherson1575 Рік тому

    'Promo SM'

  • @SPACE_VHS
    @SPACE_VHS Рік тому

    This came up on recommended, hope everyone passes 🇸🇪🇸🇪🇸🇪🇸🇪

  • @walidtajuri6832
    @walidtajuri6832 Рік тому

    Would you please, sir, add the link to the code here?

  • @alejandrogiraldo5987
    @alejandrogiraldo5987 Рік тому

    Amazing labor Kevin, teach and explain QC is a lil bit dificult, sometimes is difficult to understand the big picture for whom are not in the wave, but thanks for always creating content and meetings to surf!

  • @konstiderfeineherr2795
    @konstiderfeineherr2795 Рік тому

    JJ is a living W

  • @rojatre
    @rojatre 2 роки тому

    Well presented.

  • @sallycombs4257
    @sallycombs4257 2 роки тому

    p͓̽r͓̽o͓̽m͓̽o͓̽s͓̽m͓̽ 🤷

  • @jcminvestments9078
    @jcminvestments9078 2 роки тому

    Caltech is the best one and neurology is my field.

  • @jenifferpettinger1591
    @jenifferpettinger1591 2 роки тому

    p̶r̶o̶m̶o̶s̶m̶