From Atoms To Words #8: Fellow Scientists, Do We Really Need AI?

Welcome to From Atoms to Words! This month, we continue our exploration of the shift in science, from serendipity to AI-driven discoveries. AI is all around us – in drug discovery, material design, protein structure prediction... But is AI truly essential? Why, and in what ways? In this edition, we're going to tackle these hot questions, diving into AI's role in drug discovery, giving a glimpse into the immensity of chemical space, and exploring the evolution of computational chemistry in the era of machine learning. Ready to jump in? Let's go!

AI in Drug Discovery: Chasing Dreams, Facing Realities

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12-15 years. That's the average time it takes to transform a spark of insight into a brand-new drug on pharmacy shelves. 12-15 years and an avalanche of dollars. Roughly 2.5 billion, give or take. 💰

🤔 But why's drug discovery such a challenge?

First off, you've got to identify the biological target involved in a disease. This could be a fragment of a nucleic acid or a protein-based entity, like an enzyme or a receptor. Just identifying that target is an epic quest on its own.

Once you have your target, you need to design a drug that can grapple with the disease. And that's when the molecular mayhem really kicks off.

Screening the chemical space to find a specific molecule is like trying to find a grain of sand of a very specific blue in the Sahara desert. Quite literally.

Chemical space is vast. Insanely vast, featuring a staggering 10^22 to 10^60 possible combinations. Your potential wonder drug is hidden in that gigantic desert.

💊 So, what's the game plan?

You dive into the discovery stage with a legion of candidates and start whittling them down via techniques like high-throughput screening of compound libraries against the biological targets.

For most drugs, the spotlight's on small molecules (just like our beloved cisplatin). These molecular marvels are then passed to the medicinal chemists, who tweak them into more effective forms, smoothing out any rough edges.If that work pans out, you level up to preclinical trials.

This phase is all about running a wide range of tests tracking the drug candidate's journey in vivo — its metabolism path, interactions, and, ultimately, its exit strategy during excretion. Safety and dosage are also part of the deal, and only the molecules that clear these tests move on to the next step — the clinical trials.

Sound like a Herculean task? Well, it is. This discovery and preclinical stage takes about six years.

💻 Aiming to trim timelines, cut costs, and boost success rates, we've armed our medicinal chemists with an arsenal of computational techniques. Think computer-aided drug design, where we mix in a slew of computational methods—docking, QSAR, DFT, molecular dynamics, and more.

These computational tools have been helping, sure, but the road to fame for a potential drug is still long and winding.

🤖 Now, there's been some serious chatter about AI potentially turning those grueling preclinical stages into a cakewalk.

But is AI the real deal in drug discovery? And if it is, how exactly could AI disrupt the entire drug discovery process?

📚 Get the full scoop at ➡️ AI in Drug Discovery: Chasing Dreams, Facing Realities

Chemical Space to Material Discovery: Simulations and Machine Learning Leading the Way

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🪐 From the infinite of chemical space to material discovery: how can we use our computational power to identify, rationalize, discover & design?

In today's R&D landscape, the mission is clear: to discover new materials that will pave the way to a greener future. Progress has been steady yet laboriously slow.

So, what are the essentials to reach that future faster?

🚀 We must move beyond trial and error and embrace data-driven discovery to advance to the next level of material innovation.

Simply put, our strategy is to bolster our experimental R&D efforts with a holistic computational approach, from quantum to AI.

Our aim is to:

1️⃣ 𝐈𝐝𝐞𝐧𝐭𝐢𝐟𝐲 | High-Throughput Computation to Screen Chemical Space

↳By merging the atomistic insight of simulations with the swift response of machine learning, we can fast-track our efforts, identifying potential material gems. High-throughput computational screening is our North Star in the enormous chemical space. It transforms an overwhelming number into a sizable, more manageable set, ready for further computational scrutiny or real-world experiments.

2️⃣ 𝐑𝐚𝐭𝐢𝐨𝐧𝐚𝐥𝐢𝐳𝐞 | Multiscale Simulations to Zoom Into the Atomistic Level

↳We leverage simulations to derive the fundamental 'laws of chemistry' that govern our materials, build a foundational library of key chemical features, and compile a rich dataset to train and refine our machine learning models.

3️⃣ 𝐃𝐢𝐬𝐜𝐨𝐯𝐞𝐫 & 𝐃𝐞𝐬𝐢𝐠𝐧 | Machine Learning for Inverse Design

↳Inverse design is the forward leap in materials discovery. It's a proactive stance: envision the properties first, then seek the material to match. At the heart of this strategy lies machine learning. Depending on our goal and confidence in the outcome, our next steps are either to employ multiscale simulations for a deeper understanding or to dive straight into real-world experiments.

🤷🏼♂️ If only it were that simple.

See, a material’s story isn’t solely about its properties. It's a messy mix of interactions, production processes, and environmental conditions. It's a tale more akin to a James Joyce novel than a dry shopping list.

And yet, when we astutely craft a model of a real-world system that is both computationally feasible and reliable, we get closer to capturing the genuine essence of the material. ⚗️

Sure, chemical space might seem intimidating, and it is. But it’s also thrilling.

With each step forward, our computational techniques will become even more central to how we identify, rationalize, discover & design the materials of tomorrow.

Curious to learn more?

📚 Get the full scoop at ➡️ Chemical Space to Material Discovery: Simulations and Machine Learning Leading the Way

Is Machine Learning Going to Replace Computational Chemists?

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🎸 You heard 'Now and Then', the last Beatles song, right? Even with just Ringo and Paul still around, AI magic has brought John's voice back, and suddenly, it's like we have the Fab Four together again. 🇬🇧🪲

It's an emotional mix of nostalgic vibes and latest tech. It makes you wonder how machine learning and AI are shaking things up in many areas of human activity.

⚛️ As someone who grew up with old school computational chemistry, I can't help but mull over what all this rapid machine learning progress means for the field.

It's everywhere – drug discovery, material design, protein structure prediction... Is machine learning poised to replace computational chemistry? Should we, computational chemists, start sweating?

No need to freak out, folks. It's all about how you look at it.

🙌 As long as we don't bury our heads in the sand of old-school thinking and are willing to embrace the new wave, machine learning is the next big adventure.

In any creative field – be it the arts, music, or science – machine learning, at the end of the day, is just a tool (but is it, really?).

🎨 A really powerful one that, if we handle it smartly and thoughtfully, could open up a world of possibilities, in computational chemistry included.

To me the future of computational chemistry in the era of machine learning looks like this vast, untamed wilderness out there waiting for the brave to explore.

So, let's not just sit back. Let's be those brave explorers. 🧭

📚 Get the full scoop at ➡️ Is Machine Learning Going to Replace Computational Chemists?

+3 Bonus Stories

1️⃣ The Lifesaving Hunch: How Rosenberg’s Unexpected Discovery of Cisplatin Changed Medicine

The discovery of cisplatin: a story of human curiosity and ingenuity inspiring a pivotal turn in medical history. [Read More]

2️⃣ Curiosity, Ingenuity, Persistence – Andre Geim’s Random Walk to the Discovery of Graphene

From Friday night experiments to the Nobel Prize, let's walk Andre Geim's random walk to the discovery of graphene. [Read More]

3️⃣ Do We Live in a Simulation? Yes, No, Maybe: Insights from 12 Leading Experts

Dive into the age-old debate: "Do we live in a simulation?" Explore experts' views and join me as we weigh the arguments for and against. [Read More]

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[Arturo Robertazzi | From Atoms To Words]