AI for Drug Design - Lecture 16 - Deep Learning in the Life Sciences (Spring 2021)
ฝัง
- เผยแพร่เมื่อ 8 มิ.ย. 2024
- MIT 6.874/6.802/20.390/20.490/HST.506 Spring 2021 Prof. Manolis Kellis
Guest lecture: Wengong Jin
Deep Learning in the Life Sciences / Computational Systems Biology
Playlist: • MIT Deep Learning in L...
Latest slides and course today: compbio.mit.edu/6874
Spring 2021 slides and materials: mit6874.github.io/
0:00 Introduction
1:16 Drug discovery
3:57 Computational drug discovery
17:14 Deep learning
22:27 Antibiotic discovery
26:30 Traditional approaches
30:16 Antibiotic discovery using GNNs
47:32 Biology aware models
54:35 Incorporating biology and chemistry
1:05:54 De novo drug design
1:06:57 Graph generation
1:13:51 Junction tree variational autoencoder
1:25:03 Conclusion - วิทยาศาสตร์และเทคโนโลยี
This is an excellent resource for getting to know GNNs in drug de novo drug design, thank you!
It's great that lectures like these are publicly available.
The guy interrupts waay too much, he's so annoying..my goodness
@@LAinLA86 As a ML scientist working in the same area of research I found Prof Kellis' questions really insightful. Many of the questions he asked were what I wanted to ask at those moments.
Brilliant! Thanks for making this available
thank you so much for sharing this valuable lecture about GNNs in drug discovery. The idea of junction tree VAE is awesome and making sense
Amazing lecture !
very helpful! great lecture!
Thanks. Super quality.
Thank you for sharing this!
Thank you. The lecture is great.
Awesome lecture!
Good Information - Thanks for the presentation.
great content
Great lecture. For the compound on the right at 47.36min with better MIC, did you observe the different moieties by cleaving the azo bond? It is my guess that the aminosulphonamide or the hydrazine analog may be the actual active drug and not the whole compound shown.
life saving lecture omg
keep it up
An amazing lecture indeed. I just wanted to clarify as to how the viral/host targets are represented or vector encoded in the combonet model.
Great lecture! Thank you very much for uploading this. I just got two questions:
1. We know that drugs are 3D when they bind to their targes. But the grape in GNN is a 2D representation of drug structures. For some drugs which are only active in one enantiomer form not the other one, the 2D graph will be the same for the two enantiomers. How can GNN capture this stereochemical difference?
2. Remdesivir used in the ComboNet on slide 36 is a prodrug, its active form is a metabolite which is a triphosphate. I suppose most of the drugs used in the training are not prodrug, so I'm curious about whether remdesivir or it's active form is used in the work? Did the author consider this?
Add geometric data such as bond angle etc.. to the bond embedding vector is one approach perhaps
@@jessedeng3300 Thanks for the reply. I learned afterwards that chirality information of atoms (nodes) are used as descriptor.
Adding 3D hasn't seen much progress so far. One reason is 2D structure is informative enough, the other is 3D structures is deeply corresponding to its 3D conformer, which is very hard to determine or predict.
How this is different from QSAR using graph theory descriptors with 1D,2D, and 3D info
is ComboNet publicly accessible?
Ambitious I like...🤓
Too bad had to skip over the COVID part due to PTSD 🤣
Thats unfortunate, you have to work with those types in the top left corner. Yeesh.
Who are you referring to?
2.6B only when considering the failures, so for a startup looking to raise money, its not as important. Rather the average cost to get one drug into FIH trials is more relevant, and then how much to get the $ to run the phase 1 trial. The 2.6B is more a big pharma number.
1000th like
😉
Hi Wengong, I’m thinking about starting up a Biotech company for a widespread unmet medical need and am currently seeking capital. If and when that happens, I will be seeking scientists who can assist with advancing a therapeutic solution for this critical global disease.. I think GNN would be a worthwhile starting point in my endeavor, might you be interested in assisting? Kind regards, Jennifer
I'm sorry Dave, I'm afraid I can't do that.
"it comes from a novel called Space Odissey..."
Zoomers will zoom.
why look for a cv19 drug? There are so many more deadly conditions to treat that need drug discovery!