The hottest Drug Discovery Substack posts right now

Elsie Biotechnologies uses computational design tools and high-throughput experimental approaches to develop drugs.
Partnerships with large pharmaceutical companies like GSK can significantly impact the progress of smaller biotech companies.
Oligonucleotide therapies represent the future of drug development, promising safer and more effective treatments.

AI and machine learning are revolutionizing drug discovery by speeding up the identification of potential treatments, leading to big rewards for those in the industry.
Building a successful biotech company requires patience, determination, and significant funding, often with a focus on research and development before revenue generation.
Investors in biotech companies must be prepared for a long journey of constant failures and successes, akin to the process of drug discovery, with potential acquisitions being key outcomes.

Scott Alexander cautions against rash actions in abolishing the FDA.
The hardline libertarian proposal suggests turning the FDA into an informational consumer protection agency.
Removing efficacy testing requirements could lead to cheaper drugs and quicker medical innovation.

Vial is working to reduce the cost of clinical trials by innovating new technology.
Vial's Act I focuses on structuring, digitizing, and automating the clinical trial process to drive efficiency.
Vial's Act II, Battery Bio, aims to revolutionize drug discovery by integrating software and advanced technologies in a vertically integrated approach.

Enveda Biosciences focuses on natural product discovery for potent new medicines
The company challenges the belief that natural products are ineffective in modern drug discovery
Enveda leverages large-scale metabolomics and AI to accelerate the natural product discovery process

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AI in drug discovery has potential, but it's not a cure-all.
AI has been more successful in modality companies than in target companies.
Advances in AI-driven modality engineering show promise for the future of drug discovery.

Google DeepMind's AlphaGeometry AI system solves complex geometry problems at human Olympiad level.
Codium AI's AlphaCodium improves code generation in LLMs with test-based iterative flow.
Meta is working on open-source AGI and Microsoft Research made progress in AI-driven drug discovery.

Natural compounds from nature are a valuable source for potential new drugs.
Graph neural networks are used to predict mass spectra from molecular structure in a novel way.
The graph neural network approach provides a promising path for computational drug discovery advancements.

Phenotypic screening focuses on identifying specific physical or biochemical traits of interest for drug discovery.
Key rules for effective phenotypic screens include selecting relevant cell models, designing disease-specific assays, and defining clinical-like endpoints.
Advancing phenotypic screening requires improving throughput of complex models, developing translational disease models, enhancing proteomic tools, and integrating phenotypic and target-based screening.