0.25
0.5
0.75
1.25
1.5
1.75
2
Deep learning for computational chemistry: compound representation, ADMET profiles and automatic optimization
Published on Feb 4, 2025109 Views
One of the main challenges in small molecule drug discovery is efficiently finding novel chemical compounds with desirable properties. Such properties can be physico-chemical (like logD or solubility)
Related categories
Presentation
Deep learning for computational chemistry00:00
Introduction00:45
Computational chemistry - 100:47
Computational chemistry - 201:00
Describing the chemical matter02:17
History of deep learning for computational chemistry - 103:37
History of deep learning for computational chemistry - 204:25
Continuous, data-driven molecular descriptors04:52
General idea 05:22
Data and representations07:28
Training the translation model - 108:18
Training the translation model - 209:23
Training the translation model - 309:38
Training the translation model - 409:51
Training the translation model - 510:55
Performance of the autoencoder embedding as molecular descriptor - 112:52
Performance of the autoencoder embedding as molecular descriptor - 213:39
Performance of the autoencoder embedding as molecular descriptor - 314:52
Performance of the autoencoder embedding as molecular descriptor - 414:54
Performance of the autoencoder embedding as molecular descriptor - 516:23
Wrap - up - 116:40
Multitask learning for ADMET prediction17:40
Absorption – Distribution – Metabolism – Excretion – Toxicity19:31
The data20:15
Different methods compared20:58
Multitask learning21:27
Motivation for a multitask approach and expected benefits22:23
Multitask learning in practice - 123:31
Multitask learning in practice - 224:16
Graph convolution operations - 126:19
Graph convolution operations - 228:57
Graph convolution operations - 329:03
Graph convolution operations - 429:31
Going back to a molecule-level representation29:54
Model evaluation30:28
Absorption – Distribution: physico-chemical properties - 131:23
Absorption – Distribution: physico-chemical properties - 232:15
Absorption – Distribution: physico-chemical properties - 333:44
Absorption – Distribution: physico-chemical properties - 434:49
Wrap - up - 237:05
Absorption – Distribution: physico-chemical properties - 538:31
Molecule Swarm Optimization (MSO)42:44
Introduction: navigating the CDDD chemical space42:48
Introduction: navigating the CDDD chemical space - 243:22
How to steer the navigation towards useful chemistry? - 144:29
How to steer the navigation towards useful chemistry? - 246:13
Particle swarm optimization 47:04
Single parameter optimization49:08
Restraining the chemical space - 150:37
Restraining the chemical space - 251:57
Restraining the chemical space - 352:48
Multi-parameter optimization - 154:21
Multi-parameter optimization - 255:51
Wrap - up - 356:34
Conclusion - 157:46
Conclusion - 257:49
Thank you58:21