Aug 18, 2023

Revolutionizing Small Molecule Manufacturing: The AI/ML Paradigm Shift

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Author: Intelligence Ventures


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Original publication here.


Small molecule manufacturing is a complex realm that bears many similarities to other types of industrial manufacturing (from automobiles to consumer packaged goods), but has historically been difficult to integrate end-to-end processing (i.e., supply chain) because of its unique challenges. Regulatory pressure from the FDA has generated a great deal of research into how small molecule synthesis and purification can mimic their industrial counterparts, however the industry still has not been able to implement a continuous manufacturing solution that solves the technical challenges associated with traditional batch-to-batch production. This sector is begging for AI/ML disruption which will increase production efficiency (material cost and manufacturing time), improve drug purity, reduce the probability of contamination, optimize formulation in silico, and reduce the footprint of industrial manufacturing facilities. But to truly grasp the value AI/ML brings, we need a comprehensive understanding of the small molecule lifecycle.

A Glimpse into the Small Molecule Lifecycle:

  • Discovery: It all begins with identifying novel drug substances and predicting their efficacy, toxicokinetics, and clinical activity. [1]
  • Manufacturing of Bulk Drug Substance: This involves both upstream and downstream processes, from synthesis to purification. In small molecule development, raw materials are synthesized into the active pharmaceutical ingredient, then filtered, purified, and formulated into the final drug product. In biologics, in vivo systems (e.g., bacteria) are used to produce recombinant proteins and peptides, which are then subject to downstream chromatographic purification.
  • Final Drug Product Formulation: This is the concluding phase, focusing on the creation of the consumable drug product. [2]
  • Supply Chain Management: Monitoring market demand, adjustments to supply, quality control, packaging, and distribution.
  • Quality Control: Sampling through products to make sure the final product meets regulatory standard.

At each juncture, AI/ML plays a pivotal role:


AI is trained on historical datasets of molecular structure and physiochemistry, identifying correlates between these factors and activity against known targets and putative substrates. Ultimately, the goal is to form predictions about the potential efficacy and safety of de novo drug candidates. In small molecule manufacturing, you will frequently see these two terms:

  • Retrosynthesis: This is a technique used to break down complex molecules into simpler ones. Essentially, it’s reverse engineering a molecule to understand its building blocks. [5]
  • Synthetic Route Optimization: In essence, it's finding the most efficient and cost-effective way to create a molecule. [6] AI/ML can identify optimal paths that might be overlooked by traditional methods.


AI streamlines processes, cuts costs, boosts purity, optimizes production schedules, and minimizes contamination risks. [3]


AI predicts a drug product's in vitro behavior, determining physical characteristics like hardness and moisture content, which will affect the dissolution, diffusion, and distribution (pharmacokinetics) In turn, this will inform the route of administration and material composition of the final formulation (e.g., fillers, excipients, and co-factors).

Supply chain management

Traditional replenishment times, which average 75 days in the pharmaceutical sector, can be optimized. AI/ML provides warehouses with better planning tools for delivery routes, real-time monitoring of market supply and demand, and automation that takes into account drug expiration dates and specific storage requirements. [4]

Quality control

AI/ML can replace human efforts and seamlessly integrate into the production line. Recently Pfizer has adopted AI-based imaging analysis tools to detect defects of their tablet coating.

Global Demand for a Paradigm Shift

In the face of global challenges, such as the COVID pandemic and resulting Tylenol shortages, it becomes evident that pharmaceutical companies need robust systems to monitor and meet market demands. [7] AI/ML is that robust system.

The most transformative element perhaps is the blend of AI with the principles of Industry 4.0 – the current trend of automation and data exchange in manufacturing technologies. This blend is backed by real-time sensors, automatic robotics, digitalization of factory controls, and the Internet of Things. [8]

A significant portion of manufacturing is outsourced to Contract Manufacturing Organizations (CMO). With AI/ML's entry, the face of this sector is bound to change. By 2028, the CMO market size is projected to reach $210.5 Billion. [9]

In recent years, Continuous Manufacturing (CM) has been replacing traditional Batch Manufacturing due to its drastically improved efficiency, simplicity, quality assurance, and reduced probability of contamination. CM involves streamlining each technical aspect (e.g., synthesis, crystallization, filtration, and downstream processing - see Figure below) into a single end-to-end process that obviates the clunky “start-and-stop” nature of Batch Manufacturing, continuously monitoring the physical properties of the equipment and chemical properties of the raw material/active pharmaceutical ingredient without human intervention. Not only does this speed up production time, it improves the purity of the final drug product, reduces the chances of contamination, reduces inventory/storage, and requires a significantly smaller manufacturing plant footprint.The advantages are obvious, but there are still technical issues to work out (for example, how do we guarantee that the process is proceeding as intended if we can’t take samples at each step to diagnose problems? Can we trust the continuous monitoring? And if so, how do we know if a deviation has ruined the batch without manual intervention and testing?).

Perhaps AI/ML can help… [10]

“In order to entirely exploit the advantages of CM, the mainly separately developed processes need to be integrated to form end-to-end systems from the raw materials to the final dosage forms. However, even the integration of two technological steps is a challenging task. The development of end-to-end systems requires deep process understanding and a holistic approach toward process development and optimization.”
~ Integrated Continuous Pharmaceutical Technologies—A Review [3]

This need for a “deep process understanding” can be facilitated by AI/ML to fully integrate each step of standard batch processing into a single end-to-end system (CM). For example, spectroscopy (using in-line spectrometer probes) in combination with ML can “non-invasively” monitor process parameters such as metabolite and raw material concentrations (which, according to the article below, cannot be measured directly using Raman linear regression), obviating the material sampling/testing steps taken in standard batch processing. Similarly, in-line probes with high-frequency sensors and be installed to measure the physical factors that relate to manufacturing efficiency, such as sound, vibration, and electricity consumption. This physical data can be combined with ML to predict the latest possible time for maintenance or repair of an asset.

Big Pharma Bets on AI to Make Drugs Faster, Cheaper

The largest Contract Drug Manufacturing Organizations in the world are working furiously to implement AI/ML into their process to turn traditional Batch Manufacturing on its head.

In an article titled “Growth of Artificial Intelligence in Pharma Manufacturing, Lonza highlights the profound impact of AI/ML in drug manufacturing. Their idea of steering the process towards a “golden batch” perfectly encapsulates the potential of AI interventions, whether in small molecule production or biologic.

  • AI/ML aids in synthetic route optimization, retrosynthesis, toxicological assessments, and formulation design in small-molecule development.
  • Machine Learning is instrumental in developing controlled-release tablets, assessing key factors for predicting a tablet's in vitro behavior.
  • Spectroscopical methods, like Raman combined with ML algorithms, enable constant monitoring of critical process parameters [6].
  • ML can predict maintenance needs of assets, thus reducing equipment downtime and boosting overall availability [7].

Deciphering AI: The Revolution in Biopharma and Manufacturing

In today's rapidly evolving technological landscape, certain tides are reshaping industries in ways previously unimagined. Biopharma and manufacturing stand at the cusp of such a transformation, driven by the juggernaut of artificial intelligence (AI).

At Intelligence Ventures, we have identified and met with some of the most promising trailblazers involved in this revolution. Let's venture into this landscape, discreetly navigating the waters of the most promising AI-driven endeavors in this realm (without revealing our treasure map, of course!).

Mapping Uncharted Territories

  • The Crystal Mappers: One enterprise recently secured a notable $500K from an accelerator program. Their focus? Harnessing AI and ML to dive deep into molecular simulations, targeting drug discovery. Their capabilities in predicting molecular crystal polymorphism and thermodynamics might just reshape how drugs are discovered, by making the entire process more precise and faster.
  • The Digital Architects: With a robust backing of $3.8M, another firm is channeling its energies to conjure digital replicas of manufacturing facilities. Their aspiration is to deploy AI-driven decision-making processes, potentially streamlining manufacturing processes and yielding considerable cost savings.
  • The Watchful Protectors: One player, boasting a $1.2M seed round, is all set to bring a paradigm shift in biopharmaceutical quality control. Their machine vision, fortified by AI, eyes every minuscule detail to spot contamination, safeguarding the sanctity of medicines.
  • The Formulation Maestros: With an impressive backing of $4M, this enterprise is breaking boundaries in drug formulation. Their approach? Combining AI and automation to hasten drug formulations and assuage the challenges tied to scaling manufacturing operations.
  • The Acoustic Explorers: An intriguing endeavor backed by a $256K grant, this venture is tuning into the world of drugs quite literally. They aim to decipher acoustic vibrations from drug formulations, allowing for deeper insights and potentially reshaping drug understanding.

Decoding the Future

What do these endeavors spell for biopharma and manufacturing? A silent revolution, perhaps. The numbers reflect not just investor confidence but also the impending wave of transformation these enterprises could usher. The silent signals we've been picking up, here at Intelligence Ventures, from this landscape are loud and clear: AI's potential in healthcare and manufacturing is monumental. And while we cherish our unique vantage point, we're equally eager to champion the change these players might bring about.

Intelligence Ventures' Mastery and Vision

Leading the AI Renaissance in Pharma Manufacturing:

In the intricate tapestry of pharmaceutical manufacturing, the infusion of AI isn't just another thread but the very loom on which the future is being woven. Understanding its integration is one thing; mastering it is another. At Intelligence Ventures, our depth of knowledge isn't just based on theories or observations. We possess a precise and comprehensive grasp of exactly how AI will redefine pharmaceutical manufacturing's every facet.

This isn't just about being ahead of the curve; it's about defining it. As the vanguards of this revolution, we don't merely have front row seats – we are directing the play. With Intelligence Ventures, stakeholders aren't merely watching a transformative epoch unfold; they're partnering with the very architects of this change. Embrace the expertise that can turn vision into reality. Join us, and be instrumental in crafting the pharmaceutical industry's future.


  1. Domokos et al. (2021). Integrated Continuous Pharmaceutical Technologies – A Review. ACS Publications.
  2. Bannigan, P., Aldeghi, M., Bao, Z., Häse, F., Aspuru-Guzik, A., & Allen, C. (2021). Machine learning directed drug formulation development. Advanced Drug Delivery Reviews, 175, 113806.
  3. Esmonde-White, K. et al. (2021). The role of Raman spectroscopy in biopharmaceuticals from development to manufacturing. Analytical Bioanalytical Chemistry.
  4. US Food & Drug Administration. 2023. Artificial Intelligence in Drug Manufacturing. Discussion Paper.
  5. Smith, M. D. (2018). Introduction to retrosynthesis. Organic Chemistry.
  6. Ackerman-Biegasiewicz LKG, et al. (2020). Organic Chemistry: A Retrosynthetic Approach to a Diverse Field. ACS Cent Sci.
  7. Merck KGaA, Darmstadt, Germany. 2020. Digitalization of Production: Merck KGaA, Darmstadt, Germany, and Siemens Collaborate.Bayer digitalization of Manufacturing
  8. Arden, N. S., Fisher, A. C., Tyner, K., Yu, L. X., Lee, S. L., & Kopcha, M. (2021). Industry 4.0 for pharmaceutical manufacturing: Preparing for the smart factories of the future. International Journal of Pharmaceutics, 602, 120554.CMO Market Report
  9. US Food & Drug Administration. 2023. Artificial Intelligence in Drug Manufacturing. Discussion Paper.
  10. Kavasidis, I. et al. 2023. Predictive Maintenance in Pharmaceutical Manufacturing Lines Using Deep Transformers. The 6th International Conference on Emerging Data and Industry.
  11. Gottlieb, S. (2019). FDA Statement on FDA’s Modern Approach to Advanced Pharmaceutical Manufacturing.

More about Intelligence Ventures

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Intelligence Ventures is an emerging venture capital firm dedicated to cultivating innovation at the intersection of artificial intelligence and healthcare within the United States. Their commitment lies in the strategic investment and nurturing of pre-seed, seed, and Series A companies, fueling their growth and fostering the next generation of industry leaders.

Their first fund, AI Health Fund I, is focused on companies that use artificial intelligence to increase efficiencies and/or solve computationally intractable problems that place a ceiling on our ability to develop new drugs, advance them through clinical trials, and ultimately diagnose and treat patients. They are industry vertical agnostic and believe that generative AI and more specific ML models can be used to accelerate innovation in biotech, pharma, medtech, and diagnostics.

For more information, visit their website at or reach out to for any inquiries.

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