15 research outputs found
Barking up the right tree: An approach to search over molecule synthesis DAGs
When designing new molecules with particular properties, it is not only
important what to make but crucially how to make it. These instructions form a synthesis directed acyclic graph (DAG), describing how a large vocabulary of simple building blocks can be recursively combined through chemical reactions to create more complicated molecules of interest. In contrast, many current deep generative models for molecules ignore synthesizability. We therefore propose a deep generative model that better represents the real world process, by directly outputting molecule synthesis DAGs. We argue that this provides sensible inductive biases, ensuring that our model searches over the same chemical space that chemists would also have access to, as well as interpretability. We show that our approach is able to model chemical space well, producing a wide range of diverse molecules, and allows for unconstrained optimization of an inherently constrained problem: maximize certain chemical properties such that discovered molecules are synthesizable
Metro: Memory-Enhanced Transformer for Retrosynthetic Planning via Reaction Tree
Retrosynthetic planning plays a critical role in drug discovery and organic
chemistry. Starting from a target molecule as the root node, it aims to find a
complete reaction tree subject to the constraint that all leaf nodes belong to
a set of starting materials. The multi-step reactions are crucial because they
determine the flow chart in the production of the Organic Chemical Industry.
However, existing datasets lack curation of tree-structured multi-step
reactions, and fail to provide such reaction trees, limiting models'
understanding of organic molecule transformations. In this work, we first
develop a benchmark curated for the retrosynthetic planning task, which
consists of 124,869 reaction trees retrieved from the public USPTO-full
dataset. On top of that, we propose Metro: Memory-Enhanced Transformer for
RetrOsynthetic planning. Specifically, the dependency among molecules in the
reaction tree is captured as context information for multi-step retrosynthesis
predictions through transformers with a memory module. Extensive experiments
show that Metro dramatically outperforms existing single-step retrosynthesis
models by at least 10.7% in top-1 accuracy. The experiments demonstrate the
superiority of exploiting context information in the retrosynthetic planning
task. Moreover, the proposed model can be directly used for synthetic
accessibility analysis, as it is trained on reaction trees with the shortest
depths. Our work is the first step towards a brand new formulation for
retrosynthetic planning in the aspects of data construction, model design, and
evaluation. Code is available at https://github.com/SongtaoLiu0823/metro
Pipelines for Procedural Information Extraction from Scientific Literature: Towards Recipes using Machine Learning and Data Science
This paper describes a machine learning and data science pipeline for
structured information extraction from documents, implemented as a suite of
open-source tools and extensions to existing tools. It centers around a
methodology for extracting procedural information in the form of recipes,
stepwise procedures for creating an artifact (in this case synthesizing a
nanomaterial), from published scientific literature. From our overall goal of
producing recipes from free text, we derive the technical objectives of a
system consisting of pipeline stages: document acquisition and filtering,
payload extraction, recipe step extraction as a relationship extraction task,
recipe assembly, and presentation through an information retrieval interface
with question answering (QA) functionality. This system meets computational
information and knowledge management (CIKM) requirements of metadata-driven
payload extraction, named entity extraction, and relationship extraction from
text. Functional contributions described in this paper include semi-supervised
machine learning methods for PDF filtering and payload extraction tasks,
followed by structured extraction and data transformation tasks beginning with
section extraction, recipe steps as information tuples, and finally assembled
recipes. Measurable objective criteria for extraction quality include precision
and recall of recipe steps, ordering constraints, and QA accuracy, precision,
and recall. Results, key novel contributions, and significant open problems
derived from this work center around the attribution of these holistic quality
measures to specific machine learning and inference stages of the pipeline,
each with their performance measures. The desired recipes contain identified
preconditions, material inputs, and operations, and constitute the overall
output generated by our computational information and knowledge management
(CIKM) system.Comment: 15th International Conference on Document Analysis and Recognition
Workshops (ICDARW 2019
End-to-End Differentiable Proving
We introduce neural networks for end-to-end differentiable proving of queries
to knowledge bases by operating on dense vector representations of symbols.
These neural networks are constructed recursively by taking inspiration from
the backward chaining algorithm as used in Prolog. Specifically, we replace
symbolic unification with a differentiable computation on vector
representations of symbols using a radial basis function kernel, thereby
combining symbolic reasoning with learning subsymbolic vector representations.
By using gradient descent, the resulting neural network can be trained to infer
facts from a given incomplete knowledge base. It learns to (i) place
representations of similar symbols in close proximity in a vector space, (ii)
make use of such similarities to prove queries, (iii) induce logical rules, and
(iv) use provided and induced logical rules for multi-hop reasoning. We
demonstrate that this architecture outperforms ComplEx, a state-of-the-art
neural link prediction model, on three out of four benchmark knowledge bases
while at the same time inducing interpretable function-free first-order logic
rules.Comment: NIPS 2017 camera-ready, NIPS 201
Beyond Games: A Systematic Review of Neural Monte Carlo Tree Search Applications
The advent of AlphaGo and its successors marked the beginning of a new
paradigm in playing games using artificial intelligence. This was achieved by
combining Monte Carlo tree search, a planning procedure, and deep learning.
While the impact on the domain of games has been undeniable, it is less clear
how useful similar approaches are in applications beyond games and how they
need to be adapted from the original methodology. We review 129 peer-reviewed
articles detailing the application of neural Monte Carlo tree search methods in
domains other than games. Our goal is to systematically assess how such methods
are structured in practice and if their success can be extended to other
domains. We find applications in a variety of domains, many distinct ways of
guiding the tree search using learned policy and value functions, and various
training methods. Our review maps the current landscape of algorithms in the
family of neural monte carlo tree search as they are applied to practical
problems, which is a first step towards a more principled way of designing such
algorithms for specific problems and their requirements.Comment: 38 pages, 14 figures, submitted to Springer Applied Intelligenc
Learning to Search in Reinforcement Learning
In this thesis, we investigate the use of search based algorithms with deep neural
networks to tackle a wide range of problems ranging from board games to video
games and beyond. Drawing inspiration from AlphaGo, the first computer program
to achieve superhuman performance in the game of Go, we developed a new algorithm AlphaZero. AlphaZero is a general reinforcement learning algorithm that
combines deep neural networks with a Monte Carlo Tree search for planning and
learning. Starting completely from scratch, without any prior human knowledge
beyond the basic rules of the game, AlphaZero managed to achieve superhuman
performance in Go, chess and shogi. Subsequently, building upon the success of AlphaZero, we investigated ways to extend our methods to problems in which the rules
are not known or cannot be hand-coded. This line of work led to the development
of MuZero, a model-based reinforcement learning agent that builds a deterministic
internal model of the world and uses it to construct plans in its imagination. We
applied our method to Go, chess, shogi and the classic Atari suite of video-games,
achieving superhuman performance. MuZero is the first RL algorithm to master
a variety of both canonical challenges for high performance planning and visually complex problems using the same principles. Finally, we describe Stochastic
MuZero, a general agent that extends the applicability of MuZero to highly stochastic environments. We show that our method achieves superhuman performance in
stochastic domains such as backgammon and the classic game of 2048 while matching the performance of MuZero in deterministic ones like Go
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Chemical Information Bulletin
Periodic supplement for "the regular journals of the American Chemical Society," containing annotated bibliographies of chemical documentation literature as well as information about meetings, conferences, awards, scholarships, and other news from the American Chemical Society (ACS) Division of Chemical Literature