Knowledge Vault 6 /62 - ICML 2021
Rethinking Drug Discovery in the Era of Digital Biology
Daphne Koller
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Rethinking Drug Discovery
in the Era
of Digital Biology
Drug
Discovery
Machine
Learning
Biology
Integration
Genetics
Data
Technology
Applications
New medications
development
process. 1
Decline in
R&D
productivity. 2
AI-driven drug
development
predictions. 3
Iterative model
training
process. 13
Vast chemical
compound
synthesis. 24
On-demand compound
library
creation. 25
AI with
high-quality
data. 4
Learning data
relationships
directly. 6
AI generalization
outside training
data. 18
AI-enhanced
experimental
procedures. 20
AI in
medical
image analysis. 30
Continuous
predictions
better. 27
Bio data and
AI
integration. 5
Biology and
AI
combination. 14
In vitro
biological
models. 15
3D stem cell
cultures. 16
AI-based biology
representations. 7
Interdisciplinary
biomedical
collaboration. 23
DNA for
disease
understanding. 9
Linking genetics
to
traits. 11
Genetic-disease
causal
links. 17
Reprogramming cells
for
modeling. 29
High-quality
datasets
importance. 19
Quantitative compound
affinity
readout. 12
Modeling biology
with AI
limits. 21
Moving research
to
biotech. 22
AI for
molecular
structures. 26
AI in
protein
prediction. 28
Targeted treatments
for patient
subgroups. 8
Human
genetic
data. 10

Resume:

1.- Drug discovery: Process of developing new medications, with recent successes in vaccines, cancer treatments, and genetic therapies like for cystic fibrosis.

2.- Eroom's Law: Exponential decrease in pharmaceutical R&D productivity, with current cost per approved drug exceeding $5 billion due to high failure rates.

3.- Machine learning in drug discovery: Using AI to make better predictions at decision points throughout the drug development process.

4.- In-Citro approach: Integrating machine learning with high-quality data creation/collection to improve predictions in pharmaceutical R&D value chain.

5.- Convergence of life sciences and machine learning: Combining biological data generation tools with AI to drive insights in drug discovery.

6.- End-to-end learning: Machine learning approach that learns data representations, uncovering relationships between instances not apparent in original labeling.

7.- Human biology modeling: Using machine learning to create representations of human biology for predicting clinical impact of interventions.

8.- Patient heterogeneity: Understanding that complex diseases often comprise multiple biological processes, necessitating targeted therapeutics for specific patient subgroups.

9.- Human genetic data: Leveraging DNA sequencing and phenotypic information to understand genetic drivers of disease and drug targets.

10.- Biobanks: Large-scale collections of biological samples and data, like UK Biobank, enabling genetic and clinical research.

11.- Genome-wide association studies: Research linking genetic variants to clinical outcomes or traits, revealing genetic architecture of diseases and characteristics.

12.- Indexer technology: Provides more quantitative, sensitive readout of compound binding affinity, improving machine learning model inputs.

13.- Active learning loop: Iterative process of model training, compound selection, and testing to improve predictive capabilities in drug discovery.

14.- Digital biology: Emerging discipline combining quantitative biology measurement/intervention tools with data science/machine learning for biological insights and interventions.

15.- Organs-on-chips: In vitro models replicating multiple cell types and complex relationships, useful for studying biological systems.

16.- Organoids: 3D cell cultures derived from stem cells, forming miniature organ-like structures for more scalable, faithful organ recapitulation.

17.- Causal relations in genetics: Genetic variants associated with disease phenotypes often indicate causal relationships, with some confounding factors.

18.- Out-of-distribution robustness: Developing machine learning models that can generalize to data outside the training distribution, important for biological applications.

19.- Data quality and artifacts: Importance of high-quality, purpose-built datasets for machine learning in biology to avoid model focus on spurious correlations.

20.- Machine learning in wet lab processes: Using AI to optimize cell culture conditions and experimental procedures, enhancing data generation.

21.- Limits of learnability in biology: Philosophical question about which biological processes can be effectively modeled and predicted by machine learning.

22.- Academia to industry transition: Challenges and motivations for moving from academic research to industrial applications in biotech.

23.- Team-based approach: Importance of interdisciplinary collaboration and effective teamwork in tackling complex biomedical problems.

24.- DNA-encoded libraries: Technology enabling synthesis and testing of vast numbers of chemical compounds for drug discovery.

25.- Programmable DEL synthesis: Advanced method allowing on-demand creation of large compound libraries based on DNA-encoded instructions.

26.- Graph neural networks: Machine learning models effective at processing molecular structures for predicting binding affinities and other properties.

27.- Regression vs. classification in drug discovery: Utilizing continuous predictions (regression) can provide more informative results than binary classification.

28.- AlphaFold impact: Demonstration of machine learning's potential in solving complex biological problems like protein structure prediction.

29.- Induced pluripotent stem cells: Technology enabling creation of diverse cell types from reprogrammed adult cells, useful for disease modeling.

30.- Machine learning for histopathology: Application of AI to analyze medical images, potentially improving diagnostic accuracy and efficiency.

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