530 research outputs found
The Synthesizability of Molecules Proposed by Generative Models
The discovery of functional molecules is an expensive and time-consuming
process, exemplified by the rising costs of small molecule therapeutic
discovery. One class of techniques of growing interest for early-stage drug
discovery is de novo molecular generation and optimization, catalyzed by the
development of new deep learning approaches. These techniques can suggest novel
molecular structures intended to maximize a multi-objective function, e.g.,
suitability as a therapeutic against a particular target, without relying on
brute-force exploration of a chemical space. However, the utility of these
approaches is stymied by ignorance of synthesizability. To highlight the
severity of this issue, we use a data-driven computer-aided synthesis planning
program to quantify how often molecules proposed by state-of-the-art generative
models cannot be readily synthesized. Our analysis demonstrates that there are
several tasks for which these models generate unrealistic molecular structures
despite performing well on popular quantitative benchmarks. Synthetic
complexity heuristics can successfully bias generation toward
synthetically-tractable chemical space, although doing so necessarily detracts
from the primary objective. This analysis suggests that to improve the utility
of these models in real discovery workflows, new algorithm development is
warranted
Jahn-Teller polarons and their superconductivity in a molecular conductor
We present a theoretical study of a possibility of superconductivity in a
three dimensional molecular conductor in which the interaction between
electrons in doubly degenerate molecular orbitals and an {\em intra}molecular
vibration mode is large enough to lead to the formation of
Jahn-Teller small polarons. We argue that the effective polaron-polaron
interaction can be attractive for material parameters realizable in molecular
conductors. This interaction is the source of superconductivity in our model.
On analyzing superconducting instability in the weak and strong coupling
regimes of this attractive interaction, we find that superconducting transition
temperatures up to 100 K are achievable in molecular conductors within this
mechanism. We also find, for two particles per molecular site, a novel Mott
insulating state in which a polaron singlet occupies one of the doubly
degenerate orbitals on each site. Relevance of this study in the search for new
molecular superconductors is pointed out.Comment: Submitted to Phys. Rev.
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
Retrosynthetic Planning with Dual Value Networks
Retrosynthesis, which aims to find a route to synthesize a target molecule
from commercially available starting materials, is a critical task in drug
discovery and materials design. Recently, the combination of ML-based
single-step reaction predictors with multi-step planners has led to promising
results. However, the single-step predictors are mostly trained offline to
optimize the single-step accuracy, without considering complete routes. Here,
we leverage reinforcement learning (RL) to improve the single-step predictor,
by using a tree-shaped MDP to optimize complete routes. Specifically, we
propose a novel online training algorithm, called Planning with Dual Value
Networks (PDVN), which alternates between the planning phase and updating
phase. In PDVN, we construct two separate value networks to predict the
synthesizability and cost of molecules, respectively. To maintain the
single-step accuracy, we design a two-branch network structure for the
single-step predictor. On the widely-used USPTO dataset, our PDVN algorithm
improves the search success rate of existing multi-step planners (e.g.,
increasing the success rate from 85.79% to 98.95% for Retro*, and reducing the
number of model calls by half while solving 99.47% molecules for RetroGraph).
Additionally, PDVN helps find shorter synthesis routes (e.g., reducing the
average route length from 5.76 to 4.83 for Retro*, and from 5.63 to 4.78 for
RetroGraph).Comment: Accepted to ICML 202
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