About

Klavs F. Jensen is the Warren K. Lewis Professor in Chemical Engineering at MIT, with a focus on research in chemical engineering. His work encompasses areas such as biomedical and biotechnology, catalysis and reaction engineering, energy, environment and sustainability, materials, math and computational systems, and transport and thermodynamics. As a faculty member, he contributes to advancing knowledge in these fields and is involved in teaching and mentoring students and postdoctoral associates. His role includes leadership within the department, and he is recognized for his contributions to chemical engineering research and education.

Research topics

• Computer Science
• Chemistry
• Information Retrieval
• Artificial Intelligence
• Materials science
• Nanotechnology
• Combinatorial chemistry
• Data science
• Database
• Psychology
• Inorganic chemistry
• Photochemistry
• Physical chemistry
• World Wide Web
• Chromatography
• Cognitive science
• Process engineering
• Organic chemistry
• Chemical engineering
• Engineering

Selected publications

• Catalytic allylation of native hexoses and pentoses in water with indium

  Nature · 2025-03-26 · 7 citations

  article
  PublisherDOI

• Data-driven recommendation of agents, temperature, and equivalence ratios for organic synthesis

  Chemical Science · 2025-01-01 · 4 citations

  articleOpen access

  A machine learning framework predicts suitable agents, temperature, and equivalence ratios for reactants and agents. The model consistently outperforms strong baselines, enabling more complete and automation-ready reaction protocols.

  PublisherOA PDFDOI

• Catalytic allylation of native hexoses and pentoses in water with indium

  ChemRxiv · 2025-01-09

  preprintOpen access

  Carbohydrates are an abundant, inexpensive, and renewable biomass feedstock that could be a cornerstone for sustainable chemical manufacturing, but scalable and environmentally friendly methods that leverage these feedstocks are lacking. For example, 1-allyl sorbitol is the foundational building block for the polypropylene (PP) clarifying agent Millad® NX® 8000 which is produced on multi-ton scale annually, but the current manufacturing process requires superstoichiometric amounts of tin. , The NX 8000 additives dominate about 80% of the global clarified PP market, used in concentrations of 0.01% to 1% during PP production to improve its transparency and resistance to high temperatures, which translates to 300-30,000 tons annually. The market volume of PP in 2022 was approximately 79.01 million metric tons (MMT), with demand expected to rise by nearly 33% to 105 MMT by 2030. The cost and sustainability benefits of clarified PP are driving this demand, necessitating more clarifying agents. Herein, we report a high-yielding allylation of unprotected carbohydrates in water using a catalytic amount of indium metal and either allylboronic acid or the pinacol ester (allylBpin) as donors. Aldohexoses, aminohexoses, ketohexoses, and aldopentoses are all allylated in high yield under mild conditions, and the indium metal is recoverable and reusable with no loss of catalytic activity. Leveraging these features, this process was translated to a scalable continuous synthesis of 1-allyl sorbitol in flow with high yield and productivity through Bayesian optimization of reaction parameters.

  PublisherOA PDFDOI

• ASKCOS: an open source software suite for synthesis planning

  arXiv (Cornell University) · 2025-01-03 · 7 citations

  preprintOpen access

  The advancement of machine learning and the availability of large-scale reaction datasets have accelerated the development of data-driven models for computer-aided synthesis planning (CASP) in the past decade. Here, we detail the newest version of ASKCOS, an open source software suite for synthesis planning that makes available several research advances in a freely available, practical tool. Four one-step retrosynthesis models form the basis of both interactive planning and automatic planning modes. Retrosynthetic planning is complemented by other modules for feasibility assessment and pathway evaluation, including reaction condition recommendation, reaction outcome prediction, and auxiliary capabilities such as solubility prediction and quantum mechanical descriptor prediction. ASKCOS has assisted hundreds of medicinal, synthetic, and process chemists in their day-to-day tasks, complementing expert decision making. It is our belief that CASP tools like ASKCOS are an important part of modern chemistry research, and that they offer ever-increasing utility and accessibility.

  PublisherOA PDFDOI

• Developing Pharmaceutically Relevant Pd-Catalyzed C–N Coupling Reactivity Models Leveraging High-Throughput Experimentation

  Journal of the American Chemical Society · 2025-05-29 · 16 citations

  articleOpen accessSenior authorCorresponding

  This manuscript presents machine learning models for Pd-catalyzed C–N couplings constructed using a large, pharmaceutically relevant, structurally diverse dataset (4204 unique products) generated de novo using high-throughput experimentation. The dataset generation was enabled by the discovery of novel nanomole scale compatible automation friendly C–N coupling reaction conditions using LiOTMS as the base. The large dataset enabled the systematic evaluation of model performance using five different data-splitting strategies that were carefully designed to assess the models’ ability to both interpolate and extrapolate. The models exhibit high predictive performance across all splits as gauged by standard metrics. In addition, the models predicted with high accuracy the outcome of validation libraries that were outside the scope of the training set. Employing these models in the context of medicinal chemistry campaigns should result in significant enrichment of successful C–N couplings.

  PublisherOA PDFDOI

• Bayesian Optimization over Multiple Experimental Fidelities Accelerates Automated Discovery of Drug Molecules

  ACS Central Science · 2025-02-05 · 19 citations

  articleOpen accessSenior authorCorresponding

  Different experiments of differing fidelities are commonly used in the search for new drug molecules. In classic experimental funnels, libraries of molecules undergo sequential rounds of virtual, coarse, and refined experimental screenings, with each level balanced between the cost of experiments and the number of molecules screened. Bayesian optimization offers an alternative approach, using iterative experiments to locate optimal molecules with fewer experiments than large-scale screening, but without the ability to weigh the costs and benefits of different types of experiments. In this work, we combine the multifidelity approach of the experimental funnel with Bayesian optimization to search for drug molecules iteratively, taking full advantage of different types of experiments, their costs, and the quality of the data they produce. We first demonstrate the utility of the multifidelity Bayesian optimization (MF-BO) approach on a series of drug targets with data reported in ChEMBL, emphasizing what properties of the chemical search space result in substantial acceleration with MF-BO. Then we integrate the MF-BO experiment selection algorithm into an autonomous molecular discovery platform to illustrate the prospective search for new histone deacetylase inhibitors using docking scores, single-point percent inhibitions, and dose–response IC50 values as low-, medium-, and high-fidelity experiments. A chemical search space with appropriate diversity and fidelity correlation for use with MF-BO was constructed with a genetic generative algorithm. The MF-BO integrated platform then docked more than 3,500 molecules, automatically synthesized and screened more than 120 molecules for percent inhibition, and selected a handful of molecules for manual evaluation at the highest fidelity. Many of the molecules screened have never been reported in any capacity. At the end of the search, several new histone deacetylase inhibitors were found with submicromolar inhibition, free of problematic hydroxamate moieties that constrain the use of current inhibitors.

  PublisherDOI

• ASKCOS: Open-Source, Data-Driven Synthesis Planning

  Accounts of Chemical Research · 2025-05-21 · 49 citations

  articleOpen access

  ConspectusThe advancement of machine learning and the availability of large-scale reaction datasets have accelerated the development of data-driven models for computer-aided synthesis planning (CASP) in the past decade. In this Account, we describe the range of data-driven methods and models that have been incorporated into the newest version of ASKCOS, an open-source software suite for synthesis planning that we have been developing since 2016. This ongoing effort has been driven by the importance of bridging the gap between research and development, making research advances available through a freely available practical tool. ASKCOS integrates modules for retrosynthetic planning, modules for complementary capabilities of condition prediction and reaction product prediction, and several supplementary modules and utilities with various roles in synthesis planning. For retrosynthetic planning, we have developed an Interactive Path Planner (IPP) for user-guided search as well as a Tree Builder for automatic planning with two well-known tree search algorithms, Monte Carlo Tree Search (MCTS) and Retro*. Four one-step retrosynthesis models covering template-based and template-free strategies form the basis of retrosynthetic predictions and can be used simultaneously to combine their advantages and propose diverse suggestions. Strategies for assessing the feasibility of proposed reaction steps and evaluating the full pathways are built on top of several pioneering efforts that we have made in the subtasks of reaction condition recommendation, pathway scoring and clustering, and the prediction of reaction outcomes including the major product, impurities, site selectivity, and regioselectivity. In addition, we have also developed auxiliary capabilities in ASKCOS based on our past and ongoing work for solubility prediction and quantum mechanical descriptor prediction, which can provide more insight into the suitability of proposed reaction solvents or the hypothetical selectivity of desired transformations. For each of these capabilities, we highlight its relevance in the context of synthesis planning and present a comprehensive overview of how it is built on top of not only our work but also of other recent advancements in the field. We also describe in detail how chemists can easily interact with these capabilities via user-friendly interfaces. ASKCOS has assisted hundreds of medicinal, synthetic, and process chemists in their day-to-day tasks by complementing expert decision making and route ideation. It is our belief that CASP tools are an important part of modern chemistry research and offer ever-increasing utility and accessibility.

  PublisherOA PDFDOI

• General Chemically Intuitive Atom- and Bond-Level DFT Descriptors for Machine Learning Approaches to Reaction Condition Prediction

  Journal of Chemical Information and Modeling · 2025-06-17 · 3 citations

  articleSenior authorCorresponding

  We demonstrate the usefulness of general atom- and bond-level density functional theory (DFT) descriptors to enhance the performance of neural networks for general reaction condition prediction. We treat condition prediction as a multiclass classification task and report the performance of neural networks and random forests as evaluated by 5-fold cross-validation on a 69,935 reaction data set with 296 distinct single-component reaction condition classes and varying input embedding compositions. We show that by combining structural and general DFT descriptors, models with up to 71% fewer trainable parameter than their purely structural counterparts can provide comparable or superior weighted precision, top-1 and top-3 accuracies. Moreover, we report improvements of up to 5, 10, and 11% in weighted precision, top-1 accuracy and F1 score, respectively, for neural networks trained on hybrid representations which combine general DFT and structural descriptors, when compared to structural models with equivalent architectures and input sizes. Remarkably, the best performing neural network trained on hybrid embeddings outperforms the best purely structural model investigated despite the latter benefiting from of an embedding strategy with 267 times more data points than the one used for generating and embedding hybrid descriptors, with both strategies being unsupervised learning algorithms that share considerable conceptual and architectural similarities.

  PublisherDOI

• Earth’s ambipolar electrostatic field and its role in ion escape to space

  Nature · 2024-08-28 · 8 citations

  article
  PublisherDOI

• Author response for "Automated processing of chromatograms: a comprehensive python package with a GUI for intelligent peak identification and deconvolution in chemical reaction analysis"

  2024-08-30

  peer-review
  PublisherDOI

Recent grants

• High throughput microfluidic intracellular delivery platform

  NIH · $522k · 2013–2017

• NIH Grant RC1EB011187

  NIH · $500k · 2011

• SNM: Knowledge-based Continuous and Scalable Manufacture of Quantum Dots

  NSF · $1.3M · 2014–2019

• High throughput cell reprogramming by microfluidic jet injection

  NIH · $500k · 2009–2011

• NIH Grant R01GM101420

  NIH · $1.5M · 2019

Frequent coauthors

• Martin A. Schmidt

  76 shared

• Connor W. Coley

  Massachusetts Institute of Technology

  71 shared

• Moungi G. Bawendi

  64 shared

• William H. Green

  Massachusetts Institute of Technology

  38 shared

• Armon Sharei

  37 shared

• Róbert Langer

  Massachusetts Institute of Technology

  35 shared

• Stephen L. Buchwald

  Massachusetts Institute of Technology

  34 shared

• Jonathan P. McMullen

  Merck & Co., Inc., Rahway, NJ, USA (United States)

  30 shared

Labs

• MIT ChemEPI

Education

• Ph.D., Chemical Engineering

  Massachusetts Institute of Technology

  1990

• M.S., Chemical Engineering

  Massachusetts Institute of Technology

  1986

• B.S., Chemical Engineering

  University of Aarhus

  1984

Awards & honors

• The Neal R. Amundson Award (2023)
• National Academy of Inventors Fellow (2022)
• Corning Int’l Prize for Outstanding Work in Continuous Flow…
• John Prausnitz AIChE Institute Lecturer Award (2018)
• Elected to the National Academy of Sciences (2017)