2 Burst results for "Truck Library Library"

"truck library library" Discussed on a16z

a16z

11:38 min | 1 year ago

"truck library library" Discussed on a16z

"Of the Sixteen C Journal Club. I'm Lauren Richardson. One of our bio editors and in this episode will cover two topics. I A novel machine. Learning based approach to identify new antibiotics and second. We'll discuss two articles characterizing the novel Corona Virus causing the Current Pandemic Journal Club will cover a variety of articles every few weeks so stay tuned here and will announce its own feed to first step is my conversation with a sixteen see general partner. Dj Pandey and deal partner on the BIO team Andy Tran. We dive into a deep learning approach to antibiotic discovery by Jonathan Stokes. Regina Bardsley James Collins and colleagues and this article published and sell the authors create. A novel machine learning based method to identify new antibiotic drugs from two large databases and then validated one of their candidates. A drug named Allison showing that. It has excellent antibiotic properties both in vitro and in two different mouse models of bacterial infection. Excitingly House and has a distinct structure and appears to have a distinct mechanism of action from other antibiotics which is important given the problem of antibiotic resistance and the need to find new drugs. Our discussion of the paper covers the business of antibiotics. The methods and how deep learning can identify novel drug structures and other applications for deep learning in drug discovery and development. Though we begin with what made this paper appeal to us the first voice you'll hear is. Vj huge from this article was the breath of experimental work that was done to demonstrate the accuracy of the predictions involved then so while there has been a lot of work about using nets for predicting drug compounds. This was probably one of the landmark examples to date of a predictive perspective of approach validated. Experimentally for something. Non Trivial in terms of function typically. Doing something in vitro is pretty straightforward. But going to in Vivo models important. Step forward to convince especially drug hunters and experts in the field that this has real validity. So I think putting all those pieces together I think is what really made this a paper. Stand up why do you think it is that so difficult to identify new antibiotics? What makes a challenge is not only the scientific side but then also the business side not only are these antibiotics complex developed but the most innovative new products also cannot be sold freely. They are put on the shelf in reserve for more serious cases and they're actually dubbed these drugs of last resort and so all of these scientific and business headwinds actually make something like synthesizing. Brand new antibiotic really challenging to do. Yeah then most indications if you create a drug and it works better than other drugs that immediately becomes your top choice when you're prescribing antibiotics. They actually get put to the end of the line because they wanNA save them for when all the other drugs that are already gaining. Resistance Fail completely well in a given resistance. I think the name your snare that we're all worried about is that we don't have drugs at work as the last lines of defence. Go and we don't have any mechanism for creating new ones. The business side is very critical here. Because if there can't be a way to be rewarded for making drugs it's just hard to put hundreds of millions of dollars into doing it. It's almost like the business of making blockbuster movies that you need to have a drug. That will make enough money on the other side to be able to support all the effort that goes into it all the RND effort as well as all the effort running clinical trials whereas in this case you know once you can actually bring down the cost at least get something out of preclinical quickly. Now you have the actually opportunity for a want to go after indications. That aren't blockbusters that are small. Ones kind of almost like shift where you have certain things that are still gonna be done with like marvel and movies and so on but there's a long tail of youtubers who actually can come up with interesting content. This could be academic labs startups maybe funded through philanthropy. Maybe governments there may be now the beginnings of the potential for a long tail of new drugs coming out that go after indications. That will not be blockbusters but that will still have huge fundamental impact on humanity. There are a lot of interesting different models. We talk about here. For how technology like this coupled with a new business approach and our possible because taking could make a huge change in our ability to develop novel antibiotics. Yeah be a huge step for the industry at large once. We have more of these methods democratize for the broader scientific community. Why don't you break down for me? What the machine learning approach that they used here. And what kind of advanced does this represent took this machine learning model that they made and they traded on about twenty five hundred molecules and use that to train binary classification models to predict probability of whether it new compound would inhibit the growth of e coli or not and then turn to the truck library Library of six thousand compounds. That are ready in human clinical development for wide variety of indications and at this point the compared several different models and after narrowing down there's molecules and actually predicting toxicity using different neural networks. They've came up with this particular molecule and Howson and then thirdly lastly in the process they went on to apply machine learning motto after iteration and optimization too much broader set zinc fifteen data set with over a billion a half structures and under machine learning side. What's key here's the deep learning network that the US didn't really rely on any information about the chemical structures of molecules. It actually really built new representations called for years. A lot of people represented molecules with these fingerprint factors reflected things like presence or absence of functional. Groups are descriptors and comparable properties but relying on known fingerprints. Didn't really work that well. And that's why you know. A lot of the old antibiotic screening process gives you a lot of the same classes of molecules over and over again and what they did here they actually have these fingerprint descriptors that were built from. Scratch well you know. What you're describing is still a fingerprint. Right into dimensional vector to describe molecules. I think perhaps what's different is at deep learning approach. You can try to infer what the right descriptors should be. That's the hallmark of all of the deep. Learning approaches for drug design is at the end deep learning in general that recall even when we're just talking about conventional neural nets for image recognition the ideas that CNN's for image. Recognition versus classical computational vision. Is that in the classical approach? That person's defines what the right features are and so similarly. You know it's interesting that you can feed any representation of molecule into computer which parts of the interesting ones. You can have just like old school computer version. You could have a human being say all these in the important ones but a a beauty of DNA approach which is Ucla but also in many precursor works. That helps understand. What are the key aspects? And what are the interesting ones? And that is really. I think the big difference between what you can get in modern deep learning with machine learning versus classical machine learning with like random forest or something like that right so deep learning helps us figure out what we don't know versus focusing only on what we already know or what we think we know what makes Alison an attractive candidate for further research and development. What are some of the properties that they discovered without a doubt certainly a really potent inhibitor of e? Coli and you know. Further investigation showed that Halston has strong growth. Inhibitory effects on a wide following spectrum of pathogens. They tried it on. C. Money and which is one of the highest priority pathogens that is urgently required for in terms of antibiotics and then more. Interestingly it was even able to eradicate equal I persist yourselves that remained after episode and treatment so pretty strong efficacy and pretty low talked based on their screen. It also checks the box of something that is really structurally divergent from conventional antibiotics. And so certainly a very powerful new class of antibiotics that could potentially be strong candidate for further development. Yeah the fact that they found the antibiotic they showed it worked in vitro they showed. It worked in Vivo. And then they also did some experiments together this mechanism of action suggesting that Howson selectively disrupts the Ph potential across the bacterial membrane this saps the Proton motive force which is like the battery of the cell so all antibiotics. It's disrupting an essential cellular function but this appears to be a distinct and new function. That's being targeted super elegant work such a complete well rounded story. Yeah well I mean I think one of the things that really stands out here. Is that full stack of experiments. That they've done where it goes. All the way from looking at they might see in a dish to going through mice and one of the appealing things about studying antibiotics. And this often even pertain. Santa Viral that the animal models are pretty good with something like alzheimers. On the far extreme where animals are generally not very good. And so it's appealing that one could go. Do all of this. You know. Probably not requiring huge budget and therefore get something on the other side of that. Looks kind of intriguing beyond initial discovery? Can this kind of machine? Learning based approach be applied to other aspects of either earlier late stage drug development. Yeah it's it's brought Tommy with the fun thing about it. There's a reason why it's a broad topic because there's a broad range of things you can do. I mean you could talk about identifying targets and there's a lot of work to do there an extra shooting novel targets. It's really interesting. Time to go after novel targets. You could talk about identifying leads. Basically what's been done here that identification leads and then the testing of them. These compounds are leads but presumably. They're not drug like so they have to be optimized so there's these methods helping lead optimization and then along the way hopefully you'd want to also be screening for talks and so there's a ton of methods that are getting really surprisingly accurate basically the beautiful thing about a machine learning approach like this is at the approach for the most part is pretty agnostic to what you're predicting and that the processes you're building up can be useful one last thing and this is maybe the holy grail dream is that if you're predicting a lot of properties for a lot of different systems with a whole bunch of molecules in some multitask like framework where one model is predicting all of it. You can learn from all of it and that you develop even though you might not have a lot of data in any single project or any single area here. The sum of all this data now is huge and helps to regularise your predictions to make them less over fit and more robust such that. The sort of predictive capability that emerges from all of that is better than what anyone would be between. I think that really is the big big future as taking the fact that there is this breath of what it can do and not just recognizing all does possibilities but using all those possibilities to actually prove any one of those predictions thanks Andy and vj for joining me for this first segment so quickly. Wrap up on a high level. There are two takeaways from this article. First uncovers new candidates for future development as antibiotics most critically allison which was rigorously. Validated second demonstrates the ability of deep neural networks make accurate predictions for drug lead identification and a mechanism agnostic manner. This is a broadly useful approach with huge potential to shape. The future of drug discovery and development and further highlights the increasingly important role of a in medicine. If you enjoyed this conversation. Check OUT OUR PODCAST..

mechanism of action Allison Andy Tran Howson Sixteen C Journal Club Jonathan Stokes Regina Bardsley James Collins Lauren Richardson general partner Dj Pandey US partner CNN Ucla alzheimers truck library Library Halston Alison
Finding New Antibiotics with Machine Learning

a16z

06:07 min | 1 year ago

Finding New Antibiotics with Machine Learning

"Don't you break down for me? What the machine learning approach that they used here. And what kind of advanced does this represent took this machine learning model that they made and they traded on about twenty five hundred molecules and use that to train binary classification models to predict probability of whether it new compound would inhibit the growth of e coli or not and then turn to the truck library Library of six thousand compounds. That are ready in human clinical development for wide variety of indications and at this point the compared several different models and after narrowing down there's molecules and actually predicting toxicity using different neural networks. They've came up with this particular molecule and Howson and then thirdly lastly in the process they went on to apply machine learning motto after iteration and optimization too much broader set zinc fifteen data set with over a billion a half structures and under machine learning side. What's key here's the deep learning network that the US didn't really rely on any information about the chemical structures of molecules. It actually really built new representations called for years. A lot of people represented molecules with these fingerprint factors reflected things like presence or absence of functional. Groups are descriptors and comparable properties but relying on known fingerprints. Didn't really work that well. And that's why you know. A lot of the old antibiotic screening process gives you a lot of the same classes of molecules over and over again and what they did here they actually have these fingerprint descriptors that were built from. Scratch well you know. What you're describing is still a fingerprint. Right into dimensional vector to describe molecules. I think perhaps what's different is at deep learning approach. You can try to infer what the right descriptors should be. That's the hallmark of all of the deep. Learning approaches for drug design is at the end deep learning in general that recall even when we're just talking about conventional neural nets for image recognition the ideas that CNN's for image. Recognition versus classical computational vision. Is that in the classical approach? That person's defines what the right features are and so similarly. You know it's interesting that you can feed any representation of molecule into computer which parts of the interesting ones. You can have just like old school computer version. You could have a human being say all these in the important ones but a a beauty of DNA approach which is Ucla but also in many precursor works. That helps understand. What are the key aspects? And what are the interesting ones? And that is really. I think the big difference between what you can get in modern deep learning with machine learning versus classical machine learning with like random forest or something like that right so deep learning helps us figure out what we don't know versus focusing only on what we already know or what we think we know what makes Alison an attractive candidate for further research and development. What are some of the properties that they discovered without a doubt certainly a really potent inhibitor of e? Coli and you know. Further investigation showed that Halston has strong growth. Inhibitory effects on a wide following spectrum of pathogens. They tried it on. C. Money and which is one of the highest priority pathogens that is urgently required for in terms of antibiotics and then more. Interestingly it was even able to eradicate equal I persist yourselves that remained after episode and treatment so pretty strong efficacy and pretty low talked based on their screen. It also checks the box of something that is really structurally divergent from conventional antibiotics. And so certainly a very powerful new class of antibiotics that could potentially be strong candidate for further development. Yeah the fact that they found the antibiotic they showed it worked in vitro they showed. It worked in Vivo. And then they also did some experiments together this mechanism of action suggesting that Howson selectively disrupts the Ph potential across the bacterial membrane this saps the Proton motive force which is like the battery of the cell so all antibiotics. It's disrupting an essential cellular function but this appears to be a distinct and new function. That's being targeted super elegant work such a complete well rounded story. Yeah well I mean I think one of the things that really stands out here. Is that full stack of experiments. That they've done where it goes. All the way from looking at they might see in a dish to going through mice and one of the appealing things about studying antibiotics. And this often even pertain. Santa Viral that the animal models are pretty good with something like alzheimers. On the far extreme where animals are generally not very good. And so it's appealing that one could go. Do all of this. You know. Probably not requiring huge budget and therefore get something on the other side of that. Looks kind of intriguing beyond initial discovery? Can this kind of machine? Learning based approach be applied to other aspects of either earlier late stage drug development. Yeah it's it's brought Tommy with the fun thing about it. There's a reason why it's a broad topic because there's a broad range of things you can do. I mean you could talk about identifying targets and there's a lot of work to do there an extra shooting novel targets. It's really interesting. Time to go after novel targets. You could talk about identifying leads. Basically what's been done here that identification leads and then the testing of them. These compounds are leads but presumably. They're not drug like so they have to be optimized so there's these methods helping lead optimization and then along the way hopefully you'd want to also be screening for talks and so there's a ton of methods that are getting really surprisingly accurate basically the beautiful thing about a machine learning approach like this is at the approach for the most part is pretty agnostic to what you're predicting and that the processes you're building up can be useful one last thing and this is maybe the holy grail dream is that if you're predicting a lot of properties for a lot of different systems with a whole bunch of molecules in some multitask like framework where one model is predicting all of it. You can learn from all of it and that you develop even though you might not have a lot of data in any single project or any single area here. The sum of all this data now is huge and helps to regularise your predictions to make them less over fit and more

Howson United States Truck Library Library CNN Ucla Alzheimers Halston Alison Mechanism Of Action Tommy