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154 A. M. Bran and P. Schwaller
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Fig. 8.3 Recent advances in large language models (LLMs) have launched a new era of Transformers in chemistry. Their capabilities allow for (a) general task solvers thanks to their flexibility
and knowledge transferability [
unlimited modalities, in the form of computational tools [
70, 71]and (b) agent architectures, capable of integrating virtually
47, 72, 73
]
these endeavors aim to make the most of existing knowledge, enabling models to
learn as much as possible from the few available data points.
Interestingly, Jablonka et al. [70
] demonstrated that fine-tuning and in-context
learning can perform on par with, and in some instances even outperform, these
specialized techniques, particularly when data is limited. The high performance of
in-context learning, combined with its ease of use and flexibility, makes it one of the
most impressive applications of LLMs in chemistry to date. This technique holds the
potential to revolutionize the way machine learning is utilized in the scientific field,
by rapidly highlighting complex correlations in data.
Another application of regression, which holds significant interest in chemistry
and drug discovery cycles, is optimization. This process involves modifying an
object until a property of interest reaches a desired value. Typically, this requires
a large number of measurements of the desired property, which can be quite costly
in chemistry use-cases. Applications of this include yield/selectivity optimization
8 Transformers and Large Language Models for Chemistry and Drug … 155
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in chemical reactions and the generation of molecular candidates with target properties. Bayesian Optimization (BO) has recently been proposed as a solution to
such problems in chemistry [
However, BO requires uncertainty-calibrated regression methods, which sets it apart
from conventional regression.
In line with the concept of in-context learning, Ramos et al. [71] proposed a
system that utilizes GPT models to perform regression while also incorporating
uncertainty. This approach enables BO without the need for any feature engineering
or fine-tuning. The flexibility of this method allowed the team to perform catalyst and
molecular optimization using only the synthesis procedure of the catalyst as input.
This work represents a paradigm shift in drug discovery and molecular design. For
the first time, it showcases a direct map from the synthesis procedure into property
space, effectively overcoming issues like the synthesizability of proposed molecules,
a key limitation of structure-based generative models.
8.3.2.2 Molecular Generation
Another fascinating application of the generative capabilities of language models
is molecular generation. This area, which is of significant importance in the drug
discovery process, has been largely dominated by models that generate molecules in
the form of linear string representations, such as SMILES or SELFIES [
86], its successful application is contingent on the ability to specify substances as
graphs and their subsequent conversion to a linear string representation. However,
this approach is only suitable for a subset of organic molecules. Other substances,
such as macromolecules and materials, necessitate more comprehensive representations. A complete and accurate representation of these substances can only be
achieved by specifying atomic positions, boundary conditions, and other factors. This
requirement presents a significant challenge and limitation to the current methods
of molecular generation. To address these limitations, Flam et al. [
using language models for structure generation, directly generating them with threedimensional atomic positions. Besides being innovative and valid, the generated
structures can be obtained by training models in a variety of formats used for crystals,
proteins, and more. This work also demonstrates performance comparable to expertdesigned, state-of-the-art algorithms for molecular generation based on graphs, while
overcoming the limitations mentioned earlier.
80, 81], particularly in situations where data is small.
82–84
]. While
70, 85,
87
] proposed
8.3.3 Language Model-Powered Agents
Among the most useful emergent abilities of language models are step-by-step
reasoning, activated through chain-of-thought (CoT) prompting, and their capacity
to effectively use tools [
88]. These capabilities have been the subject of extensive
156 A. M. Bran and P. Schwaller
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research in recent years, and their application has been shown to significantly enhance
the performance of LLMs across a variety of tasks. CoT prompting is a technique
where language models are instructed to solve a task by following a sequence of
reasoning steps, rather than providing an answer in a single response [
language models in this way effectively allows them to perform symbolic operations,
much like humans perform arithmetic operations by keeping track of intermediate
steps.
The ability to use tools is another significant capability of language models [88].
This allows them to invoke external computational tools, thereby enriching their
knowledge through querying search engines, accessing calculators, and so on [
These capabilities have been demonstrated to enhance the performance of large
language models in a range of tasks that were previously inaccessible.
The recent advancements and results in revealing and exploiting the capabilities
of LLMs suggest the potential for combining some of these capabilities to create
more powerful and useful possibilities. This concept has been recently explored,
leading to the development of the Modular Reasoning, Knowledge and Language
(MRKL [
tool-using capabilities of modern LLMs. By incorporating external tools into a CoT
setting, agents of this type have recently been shown to outperform other methods
based on large language models.
unimodality issue of LLMs. Under this setting, they become capable of processing
different types of input data, making real-time decisions in simulated environments,
and even interacting with real-world robotic platforms. The solutions provided by
LLMs to tasks also become more grounded in reality, as access to certain tools
provides them with real, up-to-date information relevant to the task. This can, to some
extent, limit the tendency of these models to generate unrealistic or “hallucinated”
responses.
89]) and Reason + Act (ReAct [90]) systems, which combine the CoT and
One direct benefit of effective tool usage is that it partially overcomes the
68]. Instructing
88].
8.3.3.1 Agents in Chemistry: Unleashing the Power from Tools
Despite their strengths as text generators and task solvers, and their remarkable fewshot and zero-shot performance, these models are also well known for their high
propensity to generate false and inaccurate content, an issue that extends to easily
verifiable matters such as basic arithmetic [
limitations make the direct application of LLMs to chemistry a challenging matter.
The potential applications of large language models in chemistry were first
explored in a large-scale collaboration involving researchers from around the world,
an effort that resulted in the demonstration of 14 use-cases [
range from wrappers for computational tools, which enhance their accessibility by
allowing natural language inputs to modify behaviors, to assistants for reaction optimization, and knowledge parsers and synthesizers for scientific question answering,
among others. These are just a few of the possibilities that LLMs offer in chemistry,
88] and chemical operations [91]. These
]. The applications
92
8 Transformers and Large Language Models for Chemistry and Drug … 157
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which, when combined with existing chemistry tools and databases, significantly
increase the applicability and accessibility of computational applications.
More recently, Bran and Cox et al. [73] extended the concept of LLM-powered
agents for chemistry by curating and compiling a set of computational chemistry
tools. Their system, ChemCrow, has been shown to be capable of planning and
executing tasks in chemistry, effectively streamlining the reasoning process for
several common chemical tasks across areas such as drug and materials design and
synthesis. The authors demonstrate that this approach has a highly positive effect on
LLM’s performance for tasks in chemistry, overcoming hallucinations and grounding
their responses with data from reliable sources. Complementary approaches exist,
72
with a sharper focus on cloud lab operability [
The power of platforms like ChemCrow extends beyond merely serving as independent task solvers. They can be viewed as general chemistry assistants with the
ultimate goal of making computational tools more accessible to chemists, thereby
accelerating discovery. An additional highlight is the seamless exploitation of tool
composability that this allows. It makes it straightforward to enrich the results of one
tool with another, or to construct custom tool pipelines, all through simple requests
in natural language.
].
8.4 Outlook and Final Remarks
Advancements in neural translation models, and specially with the introduction of the
Transformer architecture, have sparked a revolution in machine learning for applications in chemistry and drug development. Analogies between chemical and natural
language, and the publication of open databases and benchmarks, have inspired
the representation of chemical tasks in the form of text, allowing straightforward
application of Transformers to problems in this field.
This revolution has occurred in three stages, differentiated by the specificity of
tasks. In a first stage, characterized by task-specificity and use of single-modality
models, applications spanned molecule-to-molecule conversion tasks, like reaction
outcome prediction and retrosynthetic planning, along with representation learning
and downstream tasks like regression and classification. Their excellent performance
and relative simplicity made them de-facto models in an array of applications.
In a second stage, researchers attempted to connect multiple additional modalities relevant to chemistry, like spectra from experiments, sequences of experimental
actions, and even natural language, opening the way for an expanded number of applications involving modalities of any sort, however still task-specific. More recently,
powered by vertiginous advancements in training and tuning of large language
models, a series of works have been published that leverage a number of capabilities from such models. Among others, these contributions showcase applications
in regression, classification, molecular generation and reaction optimization, all with
unprecedented flexibility and usually improved performance over other methods.
158 A. M. Bran and P. Schwaller
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Another direction explores the integration of virtually unlimited modalities —in
the form of tools—into agents powered by LLMs. The power of these agents has been
demonstrated through a number of diverse tasks, ranging from molecular generation
to automated organic synthesis, in an open-ended, highly customizable fashion.
By leveraging the expressivity and flexibility of natural language, this last wave
of applications aims to bridge the gap between the chemical and natural languages.
As we continue to explore and harness these capabilities, we can look forward to
a future where machine learning plays an even more integral role in accelerating
scientific discovery.
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