Drug Discovery Pro

🚀 Workshop Announcement 🚀

C-SAR: A New Strategy for Accelerating Structure Development

📅 Date: Sat. May 23, 2026
🕒 Time: 3:00 PM – 6:00 PM

Join us for an exciting scientific workshop introducing C-SAR, an innovative strategy designed to accelerate structure development and redefine modern Structure–Activity Relationship (SAR) analysis.

This workshop will provide participants with a unique opportunity to explore cutting-edge methodologies in chemical data curation, matched molecular pair (MMP) preparation, and advanced C-SAR analysis using selective HDAC6 inhibitors as a practical case study.

🔬 Workshop Highlights
-Introduction to the new concept of C-SAR
-Key differences between C-SAR and classical SAR methodologies
-Advanced chemical data curation practices
-Preparation and validation of MMP datasets
-Modern data cleaning and manipulation techniques
-Development of data diversity indexing
-Innovative approaches for C-SAR highlight extraction
-Validation of the C-SAR concept

🎯 Learning Objectives

Participants will:

Understand the definition and principles of C-SAR
Differentiate between C-SAR and traditional SAR approaches
Learn how to prepare and analyze datasets for C-SAR studies
Explore practical solutions to common C-SAR challenges
📚 Learning Outcomes

By the end of the workshop, participants will gain knowledge in:
✔ Modern data curation practices
✔ Advanced data manipulation techniques
✔ Innovative data cleansing strategies
✔ Diversity index development
✔ Cutting-edge C-SAR extraction methods
✔ Validation strategies for C-SAR studies

🗓 Workshop Agenda

3:00 – 3:30 PM | Speed-up networking with participants
3:30 – 3:40 PM | Definition of the new terminology C-SAR
3:40 – 3:50 PM | Difference between C-SAR and classical SARs
3:50 – 4:00 PM | Chemical data curation of selective HDAC6 inhibitors
4:00 – 5:00 PM | Preparation and specifications of MMPs of selective HDAC6
5:00 – 5:30 PM | C-SAR concept validation
5:30 – 6:00 PM | Q&A Session

🌟 Whether you are a researcher, graduate student, medicinal chemist, or data scientist, this workshop will offer insightful perspectives on the future of structure development and SAR innovation.

📢 Don’t miss this opportunity to explore the next generation of SAR methodologies and connect with researchers passionate about computational and medicinal chemistry innovation!

Registration is here: https://forms.gle/dKqgA5frFBur15h2A

🧪 From a weak hit in 1989 to a clinical candidate in 2019.

Thirty years of medicinal chemistry.

The story of Navoximod (GDC-0919) may be one of the most instructive examples of patience in modern drug discovery https://pubs.acs.org/doi/10.1021/acs.jmedchem.9b00662.

It began in 1989, when a high-throughput screening campaign identified a small fragment-like molecule:

4-phenylimidazole

Its activity against the immune checkpoint enzyme Indoleamine 2,3-dioxygenase 1 (IDO1) was modest:

IC₅₀ = 28 µM

For many programs, this would have been the end of the story.

But the research team chose a different path.

They began a three-decade medicinal chemistry journey.

Step by step, the molecule started teaching them.

🔬 Early SAR insights

• Adding a 2′-OH group improved potency 16-fold
• Introducing 3′-F and 5′-Cl boosted activity even further
• But potency came with a price: metabolic instability

Then the team realized something deeper.

The molecule was too flexible.

So they introduced molecular rigidification, transforming the scaffold into an imidazoisoindole system.

This structural constraint dramatically improved binding to the heme iron inside the IDO1 active site.

And then the crucial lessons emerged:

• Stereochemistry-controlled potency
• Hydrophobic interactions near Phe226 were essential
• Cellular activity required careful polarity control
• Metabolism had to be engineered away from CYP3A4 inhibition

Through dozens of iterations, the team balanced:

⚖️ potency
⚖️ cellular activity
⚖️ metabolic stability
⚖️ pharmacokinetics
⚖️ selectivity

Eventually, the optimized compound emerged:

Navoximod

• hIDO1 IC₅₀ = 0.028 µM
• Cellular IC₅₀ = 0.075 µM

A ~1000-fold improvement from the original hit.
https://chemistry-europe.onlinelibrary.wiley.com/doi/full/10.1002/cmdc.202100253

But what makes the 2019 report remarkable is not just the chemistry.

It is the humility.

The authors carefully describe:

• failed ideas
• compromises
• unexpected observations
• lessons learned along the way

They do not present the discovery as brilliance.

They present it as persistent learning from molecules.

And perhaps that is the most important lesson of all.

💡 Drug discovery rarely happens in a single insight.

Sometimes it takes 30 years of listening to the chemistry.

🧬 When Chemical Data Became the Engine of SAR

In 1962, John G. Topliss introduced medicinal chemists to a systematic way of thinking about structure–activity relationships (SAR) https://pubs.acs.org/doi/10.1021/jm01237a009.

His idea was simple but powerful: modify a parent structure step-by-step and let the biological data guide the next substitution https://pubs.acs.org/doi/10.1021/jm00280a002.

Soon after, Corwin Hansch quantified substituent effects through physicochemical parameters such as σ (electronic), π (hydrophobic), and Es (steric) — turning chemical intuition into data-driven decision making https://www.nature.com/articles/194178b0.

This synergy produced the famous Topliss decision tree, followed later by the Topliss Batchwise Scheme (TBS) — frameworks designed to optimize analogs while saving time, experimental effort, and computational resources https://pubs.acs.org/doi/10.1021/jm00214a001.

⚗️ For decades, this approach shaped medicinal chemistry:
Systematic substituent exploration
Efficient SAR generation
Guided lead optimization

But science evolves with data.

Large-scale chemical datasets and structural biology insights have revealed important limitations:
🔹 The Topliss framework assumes a stable parent chemotype (often an unfused benzene ring).
🔹 SAR derived from one chemotype does not always translate to others.
🔹 The approach struggles with molecular transformation when scaffold changes are required.
🔹 Recent studies suggest the scheme performs better for enzyme targets than for membrane receptors, where binding-site structure and X-ray complex data become critical https://pubs.acs.org/doi/10.1021/acs.jcim.7b00195.

In other words:
➡️ Early SAR was guided by chemical logic.
➡️ Modern SAR is guided by chemical data at scale.
Today, medicinal chemistry is entering a new era where:
👊 Large chemical datasets
👊 Structural biology
👊 Computational modeling
🛶 AI-driven SAR analysis

Work together to reveal how molecular structure truly drives biological activity.
The legacy of Topliss remains profound — but the future of SAR lies in integrating classical medicinal chemistry logic with modern data-driven frameworks.

🚀 The next breakthrough molecules may emerge not only from modifying structures…
But from transforming chemotypes using multidimensional chemical data.

In a recent study, a new C-SAR paradigm has been developped for converting chemotypes utilizing multidimensional chemical data, allowing us to change the chemical structure without worrying about activity loss https://www.sciencedirect.com/.../abs/pii/S0010482525005207.

🧪 A Powerful Lesson for the Scientific Community: The Story of Linrodostat
In drug discovery, breakthroughs often come not only from experiments but from scientific curiosity and intellectual honesty.

A fascinating example comes from the discovery of Linrodostat (BMS-986205), a highly potent inhibitor of Indoleamine 2,3‑dioxygenase 1 (IDO1).
Originally developed by Flexus Biosciences and later licensed to Bristol‑Myers Squibb, Linrodostat attracted significant attention because of its extraordinary cellular potency (EC50 ≈ 0.5 nM) and its irreversible su***de inhibition mechanism.

In 2018, a study led by John T. Groves and published in Proceedings of the National Academy of Sciences proposed a remarkable pharmacodynamic insight:
IDO1 may exist in two interconverting states
• a heme-bound holo form
• a heme-free apo form
The study suggested that Linrodostat primarily targets the apo form of the enzyme. https://www.pnas.org/doi/10.1073/pnas.1719190115
The reviewer of this publication was Syun‑Ru Yeh.
But the story didn’t end there.

Months later, Yeh and her team conducted their own structural investigation to explore the binding mechanism in greater detail. Instead of dismissing the earlier work, they tested the hypothesis experimentally.

Their crystallographic studies revealed something remarkable.
Rather than binding directly to an apo enzyme, Linrodostat appears to follow a multi-step binding trajectory in which it gradually displaces the heme group from the holo enzyme.

Three structural snapshots captured this process:
• Extended conformer – initial binding near the pocket entrance
(PDB 6DPR)
• Kinked conformer – entry into the heme pocket after destabilizing heme coordination
(PDB 6DPQ)
• Bent conformer – final stabilized binding mode in the Si site
(PDB 6MQ6)
https://pubs.acs.org/doi/10.1021/jacs.8b07994
The earlier structure from Groves’ study (PDB 6AZV) had already shown the enzyme without the heme cofactor, but Yeh’s work revealed how the inhibitor actually gets there https://chemistry-europe.onlinelibrary.wiley.com/doi/full/10.1002/cmdc.202100253.

Their findings beautifully illustrated:
🔬 conformational selection followed by
🔬 induced fit stabilization

🌍 But the real lesson here goes beyond structural biology.

Syun-Ru Yeh had already reviewed and approved the original manuscript.
Yet instead of assuming the story was complete, she pursued the question further in her own lab.

And when the data suggested a different mechanistic interpretation, she presented it with remarkable scientific humility—not to contradict colleagues, but to advance understanding.
No arrogance.
No confrontation.
Just better science.

📌 Lesson for researchers
Science progresses when we:
• Stay curious even after peer review
• Test ideas—even those we previously accepted
• Challenge hypotheses with data, not ego
• Communicate disagreements with respect and integrity

True scientific leadership is not about proving others wrong.
It is about moving knowledge forward.
And this story of Linrodostat and IDO1 is a beautiful reminder of that. ✨

https://www.linkedin.com/pulse/discovery-potential-drugs-against-mutant-genes-serious-xmbgf

Discovery of potential drugs against mutant genes and serious types of cancer diseases by Cheminformatics Cheminformatics has been instrumental in drug discovery, particularly in cancer research. Using computational tools allows scientists to analyze chemical data, predict the behavior of compounds, and identify potential drug candidates faster than traditional methods.